Staff Publications

Staff Publications

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

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

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

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

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

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

Making the most of imperfect data: a critical evaluation of standard information collected in farm household surveys
Fraval, Simon ; Hammond, James ; Wichern, Jannike ; Oosting, Simon J. ; Boer, Imke J.M. De; Teufel, Nils ; Lannerstad, Mats ; Waha, Katharina ; Pagella, Tim ; Rosenstock, Todd S. ; Giller, Ken E. ; Herrero, Mario ; Harris, David ; Wijk, Mark T. van - \ 2019
Experimental Agriculture 55 (2019)2. - ISSN 0014-4797 - p. 230 - 250.
Household surveys are one of the most commonly used tools for generating insight into rural communities. Despite their prevalence, few studies comprehensively evaluate the quality of data derived from farm household surveys. We critically evaluated a series of standard reported values and indicators that are captured in multiple farm household surveys, and then quantified their credibility, consistency and, thus, their reliability. Surprisingly, even variables which might be considered ‘easy to estimate’ had instances of non-credible observations. In addition, measurements of maize yields and land owned were found to be less reliable than other stationary variables. This lack of reliability has implications for monitoring food security status, poverty status and the land productivity of households. Despite this rather bleak picture, our analysis also shows that if the same farm households are followed over time, the sample sizes needed to detect substantial changes are in the order of hundreds of surveys, and not in the thousands. Our research highlights the value of targeted and systematised household surveys and the importance of ongoing efforts to improve data quality. Improvements must be based on the foundations of robust survey design, transparency of experimental design and effective training. The quality and usability of such data can be further enhanced by improving coordination between agencies, incorporating mixed modes of data collection and continuing systematic validation programmes.
LPJmL4 model output for the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation
Schaphoff, Sibyll ; Bloh, Werner von; Rammig, Anja ; Thonicke, Kirsten ; Biemans, H. ; Forkel, Matthias ; Gerten, Dieter ; Heinke, Jens ; Jägermeyr, Jonas ; Knauer, Jürgen ; Langerwisch, Fanny ; Lucht, Wolfgang ; Müller, Christoph ; Rolinski, Susanne ; Waha, Katharina - \ 2018
soil carbon - vegetation carbon - global carbon balance - permafrost distribution - discharge - fractional burned area - crop yields - global dynamic vegetation model - vegetation dynamics
LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). The data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2017a,). Additionally, these results are evaluated in the companion paper of Schaphoff et al. (2017b, http://doi.org/10.5194/gmd-2017-146). The data collection includes some key output variables made with different model setups described by Schaphoff et al. (2017b). The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for soil, vegetation, aboveground and litter carbon as well as for vegetation distribution, crop yields, sowing dates, maximum thawing depth, and fire carbon emissions. Vegetation distribution is given for each plant functional type (PFT), crop yields, and sowing dates are given for each crop functional type (CFT), respectively. Monthly data are provided for the carbon fluxes (net primary production, gross primary production, soil respiration) and the water fluxes (transpiration, evaporation, interception, runoff, and discharge) and for absorbed photosynthetically active radiation (FAPAR) and albedo.
LPJmL4 Model Code
Schaphoff, Sibyll ; Bloh, Werner von; Thonicke, Kirsten ; Biemans, H. ; Forkel, Matthias ; Gerten, Dieter ; Heinke, Jens ; Jägermeyr, Jonas ; Müller, Christoph ; Rolinski, Susanne ; Waha, Katharina ; Stehfest, Elke ; Waal, Liesbeth de; Heyder, Ursula ; Gumpenberger, Marlies ; Beringer, Tim - \ 2018
Potsdam Institute for Climate Impact Research (PIK)
soil carbon - vegetation carbon - global carbon balance - permafrost distribution - discharge - fractional burned area - crop yields - global dynamic vegetation model - vegetation dynamics
LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation.
Agricultural diversification as an important strategy for achieving food security in Africa
Waha, Katharina ; Wijk, Mark T. Van; Fritz, Steffen ; See, Linda ; Thornton, Philip K. ; Wichern, Jannike ; Herrero, Mario - \ 2018
Global Change Biology 24 (2018)8. - ISSN 1354-1013 - p. 3390 - 3400.
Farmers in Africa have long adapted to climatic and other risks by diversifying their farming activities. Using a multi‐scale approach, we explore the relationship between farming diversity and food security and the diversification potential of African agriculture and its limits on the household and continental scale. On the household scale, we use agricultural surveys from more than 28,000 households located in 18 African countries. In a next step, we use the relationship between rainfall, rainfall variability, and farming diversity to determine the available diversification options for farmers on the continental scale. On the household scale, we show that households with greater farming diversity are more successful in meeting their consumption needs, but only up to a certain level of diversity per ha cropland and more often if food can be purchased from off‐farm income or income from farm sales. More diverse farming systems can contribute to household food security; however, the relationship is influenced by other factors, for example, the market orientation of a household, livestock ownership, nonagricultural employment opportunities, and available land resources. On the continental scale, the greatest opportunities for diversification of food crops, cash crops, and livestock are located in areas with 500–1,000 mm annual rainfall and 17%–22% rainfall variability. Forty‐three percent of the African cropland lacks these opportunities at present which may hamper the ability of agricultural systems to respond to climate change. While sustainable intensification practices that increase yields have received most attention to date, our study suggests that a shift in the research and policy paradigm toward agricultural diversification options may be necessary.
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.
LPJmL4 - A dynamic global vegetation model with managed land - Part 1 : Model description
Schaphoff, Sibyll ; Bloh, Werner von; Rammig, Anja ; Thonicke, Kirsten ; Biemans, Hester ; Forkel, Matthias ; Gerten, Dieter ; Heinke, Jens ; Jägermeyr, Jonas ; Knauer, Jürgen ; Langerwisch, Fanny ; Lucht, Wolfgang ; Müller, Christoph ; Rolinski, Susanne ; Waha, Katharina - \ 2018
Geoscientific Model Development 11 (2018)4. - ISSN 1991-959X - p. 1343 - 1375.
This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates - internally consistently - the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process.
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.

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.
Uncertainty of wheat water use : Simulated patterns and sensitivity to temperature and CO2
Cammarano, Davide ; Rötter, Reimund P. ; Asseng, Senthold ; Ewert, Frank ; Wallach, Daniel ; Martre, Pierre ; Hatfield, Jerry L. ; Jones, James W. ; Rosenzweig, Cynthia ; Ruane, Alex C. ; Boote, Kenneth J. ; Thorburn, Peter J. ; Kersebaum, Kurt Christian ; Aggarwal, Pramod K. ; Angulo, Carlos ; Basso, Bruno ; Bertuzzi, Patrick ; Biernath, Christian ; Brisson, Nadine ; Challinor, Andrew J. ; Doltra, Jordi ; Gayler, Sebastian ; Goldberg, Richie ; Heng, Lee ; Hooker, Josh E. ; Hunt, Leslie A. ; Ingwersen, Joachim ; Izaurralde, Roberto C. ; Müller, Christoph ; Kumar, Soora Naresh ; Nendel, Claas ; O'Leary, Garry ; Olesen, Jørgen E. ; Osborne, Tom M. ; Priesack, Eckart ; Ripoche, Dominique ; Steduto, Pasquale ; Stöckle, Claudio O. ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Travasso, Maria ; Waha, Katharina ; White, Jeffrey W. ; Wolf, Joost - \ 2016
Field Crops Research 198 (2016). - ISSN 0378-4290 - p. 80 - 92.
Multi-model simulation - Sensitivity - Transpiration efficiency - Uncertainty - Water use

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand.

Multi-wheat-model ensemble responses to interannual climate variability
Ruane, Alex C. ; Hudson, Nicholas I. ; Asseng, Senthold ; Camarrano, Davide ; Ewert, Frank ; Martre, Pierre ; Boote, Kenneth J. ; Thorburn, Peter J. ; Aggarwal, Pramod K. ; Angulo, Carlos ; Basso, Bruno ; Bertuzzi, Patrick ; Biernath, Christian ; Brisson, Nadine ; Challinor, Andrew J. ; Doltra, Jordi ; Gayler, Sebastian ; Goldberg, Richard ; Grant, Robert F. ; Heng, Lee ; Hooker, Josh ; Hunt, Leslie A. ; Ingwersen, Joachim ; Izaurralde, Roberto C. ; Kersebaum, Kurt Christian ; Kumar, Soora Naresh ; Müller, Christoph ; Nendel, Claas ; O'Leary, Garry ; Olesen, Jørgen E. ; Osborne, Tom M. ; Palosuo, Taru ; Priesack, Eckart ; Ripoche, Dominique ; Rötter, Reimund P. ; Semenov, Mikhail A. ; Shcherbak, Iurii ; Steduto, Pasquale ; Stöckle, Claudio O. ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Travasso, Maria ; Waha, Katharina ; Wallach, Daniel ; White, Jeffrey W. ; Wolf, Joost - \ 2016
Environmental Modelling & Software 81 (2016). - ISSN 1364-8152 - p. 86 - 101.
AgMIP - Climate impacts - Crop modeling - Interannual variability - Multi-model ensemble - Precipitation - Temperature - Uncertainty - Wheat

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration
Makowski, D. ; Asseng, S. ; Ewert, F. ; Bassu, S. ; Durand, J.L. ; Li, G. ; Martre, P. ; Adam, M.Y.O. ; Aggarwal, P.K. ; Angulo, C. ; Baron, C. ; Basso, B. ; Bertuzzi, P. ; Biernath, C. ; Boogaard, H.L. ; Boote, K.J. ; Bouman, B. ; Bregaglio, S. ; Brisson, N. ; Buis, S. ; Cammarano, D. ; Challinor, A.J. ; Confalonieri, R. ; Conijn, J.G. ; Corbeels, M. ; Deryng, D. ; Sanctis, G. De; Doltra, J. ; Fumoto, T. ; Gayler, S. ; Gaydon, D. ; Goldberg, R. ; Grant, R.F. ; Grassini, P. ; Hatfield, J.L. ; Hasegawa, T. ; Heng, L. ; Hoek, S.B. ; Hooker, J. ; Hunt, L.A. ; Ingwersen, J. ; Izaurralde, C. ; Jongschaap, R.E.E. ; Jones, J.W. ; Kemanian, R.A. ; Kersebaum, K.C. ; Kim, S.H. ; Lizaso, J. ; Marcaida III, M. ; Müller, C. ; Nakagawa, H. ; Naresh Kumar, S. ; Nendel, C. ; O'Leary, G.J. ; Olesen, J.E. ; Oriol, P. ; Osborne, T.M. ; Palosuo, T. ; Pravia, M.V. ; Priesack, E. ; Ripoche, D. ; Rosenzweig, C. ; Ruane, A.C. ; Ruget, F. ; Sau, F. ; Semenov, M.A. ; Shcherbak, I. ; Singh, B. ; Soo, A.K. ; Steduto, P. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tang, L. ; Tao, F. ; Teixeira, E. ; Thorburn, P. ; Timlin, D. ; Travasso, M. ; Rötter, R.P. ; Waha, K. ; Wallach, D. ; White, J.W. ; Wilkens, P. ; Williams, J.R. ; Wolf, J. ; Ying, X. ; Yoshida, H. ; Zhang, Z. ; Zhu, Y. - \ 2015
Agricultural and Forest Meteorology 214-215 (2015). - ISSN 0168-1923 - p. 483 - 493.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].
Letter : Rising temperatures reduce global wheat production
Asseng, S. ; Ewert, F. ; Martre, P. ; Rötter, R.P. ; Cammarano, D. ; Kimball, B.A. ; Ottman, M.J. ; Wall, G.W. ; White, J.W. ; Reynolds, M.P. ; Alderman, P.D. ; Prasad, P.V.V. ; Lobell, D.B. ; Aggarwal, P.K. ; Anothai, J. ; Basso, B. ; Biernath, C. ; Challinor, A.J. ; Sanctis, G. De; Doltra, J. ; Fereres, E. ; Garcia-Vila, M. ; Gayler, S. ; Hoogenboom, G. ; Hunt, L.A. ; Izaurralde, C. ; Jabloun, M. ; Jones, C.D. ; Kersebaum, K.C. ; Koehler, A.K. ; Müller, C. ; Naresh Kumar, S. ; Nendel, C. ; O’Leary, G. ; Olesen, J.E. ; Palosuo, T. ; Priesack, E. ; Eyshi Rezae, E. ; Ruane, A.C. ; Semenov, M.A. ; Shcherbak, I. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tao, T. ; Thorburn, P. ; Waha, K. ; Wang, E. ; Wallach, D. ; Wolf, J. ; Zhao, Z. ; Zhu, Y. - \ 2015
Nature Climate Change 5 (2015). - ISSN 1758-678X - p. 143 - 147.
climate-change - spring wheat - dryland wheat - yield - growth - drought - heat - co2 - agriculture - adaptation
Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.
Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects
Makowski, D. ; Asseng, S. ; Ewert, F. ; Bassu, S. ; Durand, J.L. ; Martre, P. ; Adam, M. ; Aggarwal, P.K. ; Angulo, C. ; Baron, C. ; Basso, B. ; Bertuzzi, P. ; Biernath, C. ; Boogaard, H. ; Boote, K.J. ; Brisson, N. ; Cammarano, D. ; Challinor, A.J. ; Conijn, J.G. ; Corbeels, M. ; Deryng, D. ; Sanctis, G. De; Doltra, J. ; Gayler, S. ; Goldberg, R. ; Grassini, P. ; Hatfield, J.L. ; Heng, L. ; Hoek, S.B. ; Hooker, J. ; Hunt, L.A. ; Ingwersen, J. ; Izaurralde, C. ; Jongschaap, R.E.E. ; Jones, J.W. ; Kemanian, R.A. ; Kersebaum, K.C. ; Kim, S.H. ; Lizaso, J. ; Müller, C. ; Naresh Kumar, S. ; Nendel, C. ; O'Leary, G.J. ; Olesen, J.E. ; Osborne, T.M. ; Palosuo, T. ; Pravia, M.V. ; Priesack, E. ; Ripoche, D. ; Rosenzweig, C. ; Ruane, A.C. ; Sau, F. ; Semenov, M.A. ; Shcherbak, I. ; Steduto, P. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tao, F. ; Teixeira, E. ; Thorburn, P. ; Timlin, D. ; Travasso, M. ; Roetter, R.P. ; Waha, K. ; Wallach, D. ; White, J.W. ; Williams, J.R. ; Wolf, J. - \ 2015
In: Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) / Hillel, D., Rosenzweig, C., - 1100 p.
Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared. See more from this Division: Special Sessions See more from this Session: Symposium--Perspectives on Climate Effects on Agriculture: The International Efforts of AgMIP
Multimodel ensembles of wheat growth: Many models are better than one
Martre, P. ; Wallach, D. ; Asseng, S. ; Ewert, F. ; Jones, J.W. ; Rötter, R.P. ; Boote, K.J. ; Ruane, A.C. ; Thorburn, P. ; Cammarano, D. ; Hatfield, J.L. ; Rosenzweig, C. ; Aggarwal, P.K. ; Angula, C. ; Basso, B. ; Bertuzzi, P. ; Biernath, C. ; Brisson, N. ; Challinor, A. ; Doltra, J. ; Gayler, S. ; Goldberg, R.A. ; Grant, R.F. ; Heng, L. ; Hooker, J. ; Hunt, L.A. ; Ingwersen, J. ; Izaurralde, C. ; Kersebaum, K.C. ; Mueller, C. ; Kumar, S. ; Nendel, C. ; O'Leary, G.J. ; Olesen, J.E. ; Osborne, T.M. ; Palosuo, T. ; Priesack, E. ; Ripoche, D. ; Semenov, M.A. ; Shcherbak, I. ; Steduto, P. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tao, Fulu ; Travasso, M. ; Waha, K. ; White, J.W. ; Wolf, J. - \ 2015
Global Change Biology 21 (2015)2. - ISSN 1354-1013 - p. 911 - 925.
climate-change - crop production - impacts - yield - simulations - calibration - australia - billion - europe - grain
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
Climate-driven simulation of global crop sowing dates
Waha, K. ; Bussel, L.G.J. van; Müller, C. ; Bondeau, A. - \ 2012
Global Ecology and Biogeography 21 (2012)2. - ISSN 1466-822X - p. 247 - 259.
sown sugar-beet - s-and-h - soil-temperature - seedling emergence - tassel initiation - base temperature - high-resolution - spring wheat - corn growth - model
Aim To simulate the sowing dates of 11 major annual crops at the global scale at high spatial resolution, based on climatic conditions and crop-specific temperature requirements. Location Global. Methods Sowing dates under rainfed conditions are simulated deterministically based on a set of rules depending on crop-and climate-specific characteristics. We assume that farmers base their timing of sowing on experiences with past precipitation and temperature conditions, with the intra-annual variability being especially important. The start of the growing period is assumed to be dependent either on the onset of the wet season or on the exceeding of a crop-specific temperature threshold for emergence. To validate our methodology, a global data set of observed monthly growing periods (MIRCA2000) is used. Results We show simulated sowing dates for 11 major field crops world-wide and give rules for determining their sowing dates in a specific climatic region. For all simulated crops, except for rapeseed and cassava, in at least 50% of the grid cells and on at least 60% of the cultivated area, the difference between simulated and observed sowing dates is less than 1 month. Deviations of more than 5 months occur in regions characterized by multiple-cropping systems, in tropical regions which, despite seasonality, have favourable conditions throughout the year, and in countries with large climatic gradients. Main conclusions Sowing dates under rainfed conditions for various annual crops can be satisfactorily estimated from climatic conditions for large parts of the earth. Our methodology is globally applicable, and therefore suitable for simulating sowing dates as input for crop growth models applied at the global scale and taking climate change into account.
Global Water Availability and Requirements for Future Food Production
Gerten, D. ; Heinke, J. ; Hoff, H. ; Biemans, H. ; Fader, M. ; Waha, K. - \ 2011
Journal of Hydrometeorology 12 (2011)5. - ISSN 1525-755X - p. 885 - 899.
high-resolution - climate-change - fresh-water - resources - agriculture - vegetation - scenarios - nations - balance - trade
This study compares, spatially explicitly and at global scale, per capita water availability and water requirements for food production presently (1971-2000) and in the future given climate and population change (2070-99). A vegetation and hydrology model Lund-Potsdam-Jena managed Land (LPJmL) was used to calculate green and blue water availability per capita, water requirements to produce a balanced diet representing a benchmark for hunger alleviation [3000 kilocalories per capita per day (1 kilocalorie = 4184 joules), here assumed to consist of 80% vegetal food and 20% animal products], and a new water scarcity indicator that relates the two at country scale. A country was considered water-scarce if its water availability fell below the water requirement for the specified diet, which is presently the case especially in North and East Africa and in southwestern Asia. Under climate (derived from 17 general circulation models) and population change (A2 and B1 emissions and population scenarios), water availability per person will most probably diminish in many regions. At the same time the calorie-specific water requirements tend to decrease, due mainly to the positive effect of rising atmospheric CO(2) concentration on crop water productivity which, however, is very uncertain to be fully realized in most regions. As a net effect of climate, CO(2), and population change, water scarcity will become aggravated in many countries, and a number of additional countries are at risk of losing their present capacity to produce a balanced diet for their inhabitants.
Climate-driven simulation of global crop sowing dates
Bussel, L.G.J. van; Waha, K. ; Müller, C. ; Bondeau, A. - \ 2010
In: Proceedings of Agro 2010 the XIth ESA Congress, Montpellier, France, September 29 to September 03, 2010. - Montpellier, France : ESA - ISBN 9782909613017 - p. 943 - 944.
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