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1. Proposal and extensive test of a calibration protocol for crop phenology models

Author: Wallach, Daniel, Palosuo, Taru, Thorburn, Peter J., Mielenz, Henrike, Buis, Samuel, Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Garcia, Cécile, Gayler, Sebastian, Harrison, Matthew, Hiremath, Santosh, Horan, Heidi, Hoogenboom, Gerrit, Jansson, Per-Erik, Jing, Qi, Justes, Eric, Kersebaum, Kurt Christian, Launay, Marie, Lewan, Elisabet, Liu, Ke, Mequanint, Fasil, Moriondo, Marco, Nendel, Claas, Padovan, Gloria, Qian, Budong, Schütze, Niels, Seserman, Diana‑Maria, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Srivastava, Amit Kumar, Trombi, Giacomo, Weber, Tobias K. D., Weihermüller, Lutz, Wöhling, Thomas, Seidel, Sabine Julia, Wallach, Daniel, Palosuo, Taru, Thorburn, Peter J., Mielenz, Henrike, Buis, Samuel, Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Garcia, Cécile, Gayler, Sebastian, Harrison, Matthew, Hiremath, Santosh, Horan, Heidi, Hoogenboom, Gerrit, Jansson, Per-Erik, Jing, Qi, Justes, Eric, Kersebaum, Kurt Christian, Launay, Marie, Lewan, Elisabet, Liu, Ke, Mequanint, Fasil, Moriondo, Marco, Nendel, Claas, Padovan, Gloria, Qian, Budong, Schütze, Niels, Seserman, Diana‑Maria, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Srivastava, Amit Kumar, Trombi, Giacomo, Weber, Tobias K. D., Weihermüller, Lutz, Wöhling, Thomas, and Seidel, Sabine Julia
Abstract: A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.
Published: 2023

2. AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat

Author: Liu, Bing, Martre, Pierre, Ewert, Frank, Webber, Heidi, Waha, Katharina, Thorburn, Peter, Ruane, Alex, Aggarwal, Pramod, Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Cao, Weixing, Challinor, Andy, de Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto, Jabloun, Mohamed, Jones, Curtis, Kassie, Belay, Kersebaum, Kurt, Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O’leary, Garry, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, van der Velde, Marijn, Wang, Enli, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Liu, Bing, Martre, Pierre, Ewert, Frank, Webber, Heidi, Waha, Katharina, Thorburn, Peter, Ruane, Alex, Aggarwal, Pramod, Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Cao, Weixing, Challinor, Andy, de Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto, Jabloun, Mohamed, Jones, Curtis, Kassie, Belay, Kersebaum, Kurt, Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O’leary, Garry, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, van der Velde, Marijn, Wang, Enli, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Abstract: The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5Â°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (http://doi.org/10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.
Published: 2023

3. A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

Author: Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres, Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie, Kersebaum, Kurt, Nendel, Claas, Olesen, Jorgen, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes, Rodriguez, Alfredo, Rotter, Reimund, Ruiz Ramos, Margarita, Semenov, Mikhail, Senapati, Nimai, Siebert, Stefan, Srivastava, Amit, Stockle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres, Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie, Kersebaum, Kurt, Nendel, Claas, Olesen, Jorgen, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes, Rodriguez, Alfredo, Rotter, Reimund, Ruiz Ramos, Margarita, Semenov, Mikhail, Senapati, Nimai, Siebert, Stefan, Srivastava, Amit, Stockle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Abstract: Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models.
Published: 2023

4. Data from the AgMIP-Wheat high-yielding traits experiment for modeling potential production of wheat: field experiments and multi-model simulations

Author: Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Abstract: The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models. The R code and formatted data used for the statistical analyses are included.
Published: 2022

5. Data from the AgMIP-Wheat high-yielding traits experiment for modeling potential production of wheat: field experiments and multi-model simulations

Author: Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Ji, Zhao, Zhigan, Asseng, Senthold, Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Ji, Zhao, Zhigan, and Asseng, Senthold
Abstract: The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models. The R code and formatted data used for the statistical analyses are included. (2022-02-11).
Published: 2022

6. Evidence for increasing global wheat yield potential

Author: Guarin, Jose Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ramos, Margarita Ruiz, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Guarin, Jose Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ramos, Margarita Ruiz, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Abstract: Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.
Published: 2022

7. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

Author: Institut National de la Recherche Agronomique (France), International Maize and Wheat Improvement Center, International Wheat Yield Partnership, National Natural Science Foundation of China, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Education, Youth and Sports (Czech Republic), German Research Foundation, Biotechnology and Biological Sciences Research Council (UK), Natural Environment Research Council (UK), Academy of Finland, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Institut National de la Recherche Agronomique (France), International Maize and Wheat Improvement Center, International Wheat Yield Partnership, National Natural Science Foundation of China, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Education, Youth and Sports (Czech Republic), German Research Foundation, Biotechnology and Biological Sciences Research Council (UK), Natural Environment Research Council (UK), Academy of Finland, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Martre, Pierre
Abstract: Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.
Published: 2022

8. Evidence for increasing global wheat yield potential

Author: International Wheat Yield Partnership, International Maize and Wheat Improvement Center, Comisión Nacional de Investigación Científica y Tecnológica (Chile), Fondo Nacional de Desarrollo Científico y Tecnológico (Chile), National Natural Science Foundation of China, Ministry of Education, Youth and Sports (Czech Republic), Biotechnology and Biological Sciences Research Council (UK), Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, International Wheat Yield Partnership, International Maize and Wheat Improvement Center, Comisión Nacional de Investigación Científica y Tecnológica (Chile), Fondo Nacional de Desarrollo Científico y Tecnológico (Chile), National Natural Science Foundation of China, Ministry of Education, Youth and Sports (Czech Republic), Biotechnology and Biological Sciences Research Council (UK), Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Abstract: Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.
Published: 2022

9. Data from the winter wheat potential yield experiment in New Zealand and response to variable sowing dates and densities: field experiments and AgMIP-Wheat multi-model simulations

Author: Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Martre, Pierre
Abstract: The dataset contains 6 growing seasons of a local winter wheat cultivar ‘Wakanui’ at two farms located in the Canterbury Region of New Zealand. The data of the experiment was used in the AgMIP-Wheat Phase 4 project to evaluate the performance of an ensemble of 29 crop models to predict the effect of changing sowing dates and rates on yield and yield components, in a high-yielding environment. The treatments were managed for non-stress conditions. Data include local daily weather, soil characteristics and initial soil N conditions, crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, and yield components), and cultivar information. Simulations include both daily in-season and end-of-season results from 29 wheat crop models.
Published: 2022

10. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

Author: Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ramos, Margarita Ruiz, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Rebetzke, Greg, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ramos, Margarita Ruiz, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, and Rebetzke, Greg
Abstract: Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.
Published: 2022

11. Data from the winter wheat potential yield experiment in New Zealand and response to variable sowing dates and densities: field experiments and AgMIP-Wheat multi-model simulations

Author: Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Martre, Pierre
Published: 2022

12. Methodology to assess the changing risk of yield failure due to heat and drought stress under climate change

Author: Stella, Tommaso, Webber, Heidi, Olesen, Jorgen E., Ruane, Alex C., Fronzek, Stefan, Bregaglio, Simone, Mamidanna, Sravya, Bindi, Marco, Collins, Brian, Faye, Babacar, Ferrise, Roberto, Fodor, Nándor, Gabaldón-Leal, Clara, Jabloun, Mohamed, Kersebaum, Kurt Christian, Lizaso, Jon I., Lorite, Ignacio J., Manceau, Loic, Martre, Pierre, Nendel, Claas, Rodríguez, Alfredo, Ruiz-Ramos, Margarita, Semenov, Mikhail A., Stratonovitch, Pierre, Ewert, Frank, Stella, Tommaso, Webber, Heidi, Olesen, Jorgen E., Ruane, Alex C., Fronzek, Stefan, Bregaglio, Simone, Mamidanna, Sravya, Bindi, Marco, Collins, Brian, Faye, Babacar, Ferrise, Roberto, Fodor, Nándor, Gabaldón-Leal, Clara, Jabloun, Mohamed, Kersebaum, Kurt Christian, Lizaso, Jon I., Lorite, Ignacio J., Manceau, Loic, Martre, Pierre, Nendel, Claas, Rodríguez, Alfredo, Ruiz-Ramos, Margarita, Semenov, Mikhail A., Stratonovitch, Pierre, and Ewert, Frank
Abstract: While the understanding of average impacts of climate change on crop yields is improving, few assessments have quantified expected impacts on yield distributions and the risk of yield failures. Here we present the relative distribution as a method to assess how the risk of yield failure due to heat and drought stress (measured in terms of return period between yields falling 15% below previous five year Olympic average yield) responds to changes of the underlying yield distributions under climate change. Relative distributions are used to capture differences in the entire yield distribution between baseline and climate change scenarios, and to further decompose them into changes in the location and shape of the distribution. The methodology is applied here for the case of rainfed wheat and grain maize across Europe using an ensemble of crop models under three climate change scenarios with simulations conducted at 25 km resolution. Under climate change, maize generally displayed shorter return periods of yield failures (with changes under RCP 4.5 between -0.3 and 0 years compared to the baseline scenario) associated with a shift of the yield distribution towards lower values and changes in shape of the distribution that further reduced the frequency of high yields. This response was prominent in the areas characterized in the baseline scenario by high yields and relatively long return periods of failure. Conversely, for wheat, yield failures were projected to become less frequent under future scenarios (with changes in the return period of -0.1 to +0.4 years under RCP 4.5) and were associated with a shift of the distribution towards higher values and a change in shape increasing the frequency of extreme yields at both ends. Our study offers an approach to quantify the changes in yield distributions that drive crop yield failures. Actual risk assessments additionally require models that capture the variety of drivers determining crop yield variability and scenario cli
Published: 2021

13. Pan-European multi-crop model ensemble simulations of wheat and grain maize under climate change scenarios

Author: Webber, Heidi, Cooke, D., Ewert, Frank, Olesen, J.E., Fronzek, Stefan, Ruane, Alex C., Martre, Pierre, Collins, B., Bindi, Marco, Ferrise, Roberto, Jabloun, M., Webber, Heidi, Cooke, D., Ewert, Frank, Olesen, J.E., Fronzek, Stefan, Ruane, Alex C., Martre, Pierre, Collins, B., Bindi, Marco, Ferrise, Roberto, and Jabloun, M.
Abstract: The simulated data set described in this paper was created by an ensemble of nine different crop models: HERMES (HE), Simplace (L5), SiriusQuality (SQ), MONICA (MO), Sirius2014 (S2), FASSET (FA), 4M (4M), SSM (SS), DSSAT-CSM IXIM (IX). Simulations were performed for grain maize (six models) and winter wheat (eight models) under diverse conditions over agriculturally relevant areas in the EU-27 at a 25 x 25 km spatial resolution. Simulations were drawn from combinations of three representative concentration pathways and climate outputs from five general circulation models for time periods 2040-2069 and 2070-2099. Historical climate data was the basis for simulation years 1980-2010 and considered as a baseline. Simulation results from 1980-2010 and 2040-2069 were used to analyze crop responses to changing climatic variables and their diverging sensitivities to these variables. This data paper describes the creation, motivation and format of the simulation results to enable others to use the data set.
Published: 2020

14. Climate change impact and adaptation for wheat protein

Author: Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Ewert, Frank, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank
Published: 2019

15. Climate change impact and adaptation for wheat protein

Author: Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Ewert, Frank, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank
Published: 2019

16. Global wheat production with 1.5 and 2.0°C above pre-industrial warming

Author: Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andy J, Müller, Christoph, Ruane, Alex C, Waha, Katharina, Thorburn, Peter J, Aggarwal, Pramod K, Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Eyshi Rezaei, Ehsan, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C, Jones, Curtis D, Kassie, Belay T, Kersebaum, Kurt C, Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Naresh Kumar, Soora, Nendel, Claas, O'Leary, Garry J, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P, Semenov, Mikhail A, Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andy J, Müller, Christoph, Ruane, Alex C, Waha, Katharina, Thorburn, Peter J, Aggarwal, Pramod K, Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Eyshi Rezaei, Ehsan, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C, Jones, Curtis D, Kassie, Belay T, Kersebaum, Kurt C, Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Naresh Kumar, Soora, Nendel, Claas, O'Leary, Garry J, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P, Semenov, Mikhail A, Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Abstract: 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 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 is ther
Published: 2019

17. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author: National Science Foundation (US), National Natural Science Foundation of China, International Food Policy Research Institute (US), CGIAR (France), Institut National de la Recherche Agronomique (France), Federal Ministry of Education and Research (Germany), Biotechnology and Biological Sciences Research Council (UK), China Scholarship Council, Department of Agriculture and Water Resources (Australia), Ministero delle Politiche Agricole Alimentari e Forestali, Gorgan University, Victoria State Government, National Institute of Food and Agriculture (US), Federal Ministry of Food and Agriculture (Germany), German Research Foundation, Academy of Finland, LabEx Agro, Natural Resources Institute Finland, Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andrew J., Müller, Christoph, Ruane, Alexander C., Waha, Katharina, Thorburn, Peter, Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Jurajb, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, National Science Foundation (US), National Natural Science Foundation of China, International Food Policy Research Institute (US), CGIAR (France), Institut National de la Recherche Agronomique (France), Federal Ministry of Education and Research (Germany), Biotechnology and Biological Sciences Research Council (UK), China Scholarship Council, Department of Agriculture and Water Resources (Australia), Ministero delle Politiche Agricole Alimentari e Forestali, Gorgan University, Victoria State Government, National Institute of Food and Agriculture (US), Federal Ministry of Food and Agriculture (Germany), German Research Foundation, Academy of Finland, LabEx Agro, Natural Resources Institute Finland, Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andrew J., Müller, Christoph, Ruane, Alexander C., Waha, Katharina, Thorburn, Peter, Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Jurajb, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Abstract: 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 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 is therefore not
Published: 2019

18. Climate change impact and adaptation for wheat protein

Author: International Food Policy Research Institute (US), CGIAR (France), European Commission, Institut National de la Recherche Agronomique (France), National Natural Science Foundation of China, Federal Ministry of Food and Agriculture (Germany), Biotechnology and Biological Sciences Research Council (UK), Innovation Fund Denmark, China Scholarship Council, Ministero delle Politiche Agricole Alimentari e Forestali, Academy of Finland, Finnish Ministry of Agriculture and Forestry, Federal Ministry of Education and Research (Germany), Department of Agriculture and Water Resources (Australia), University of Melbourne, Grains Research and Development Corporation (Australia), National Institute of Food and Agriculture (US), German Research Foundation, Gorgan University, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Rötter, Reimund P., O'Leary, Garry, Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew, Kheir, Ahmed, M .S., Thorburn, Peter, Waha, Katharina, Ruane, Alexander C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Ewert, Frank, International Food Policy Research Institute (US), CGIAR (France), European Commission, Institut National de la Recherche Agronomique (France), National Natural Science Foundation of China, Federal Ministry of Food and Agriculture (Germany), Biotechnology and Biological Sciences Research Council (UK), Innovation Fund Denmark, China Scholarship Council, Ministero delle Politiche Agricole Alimentari e Forestali, Academy of Finland, Finnish Ministry of Agriculture and Forestry, Federal Ministry of Education and Research (Germany), Department of Agriculture and Water Resources (Australia), University of Melbourne, Grains Research and Development Corporation (Australia), National Institute of Food and Agriculture (US), German Research Foundation, Gorgan University, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Rötter, Reimund P., O'Leary, Garry, Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew, Kheir, Ahmed, M .S., Thorburn, Peter, Waha, Katharina, Ruane, Alexander C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank
Abstract: 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.
Published: 2019

19. Climate change impact and adaptation for wheat protein

Author: Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Ewert, Frank, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank
Published: 2019

20. Global wheat production with 1.5 and 2.0°C above pre-industrial warming

Author: Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andy J, Müller, Christoph, Ruane, Alex C, Waha, Katharina, Thorburn, Peter J, Aggarwal, Pramod K, Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Eyshi Rezaei, Ehsan, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C, Jones, Curtis D, Kassie, Belay T, Kersebaum, Kurt C, Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Naresh Kumar, Soora, Nendel, Claas, O'Leary, Garry J, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P, Semenov, Mikhail A, Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andy J, Müller, Christoph, Ruane, Alex C, Waha, Katharina, Thorburn, Peter J, Aggarwal, Pramod K, Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Eyshi Rezaei, Ehsan, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C, Jones, Curtis D, Kassie, Belay T, Kersebaum, Kurt C, Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Naresh Kumar, Soora, Nendel, Claas, O'Leary, Garry J, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P, Semenov, Mikhail A, Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Abstract: 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 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 is ther
Published: 2019

21. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Author: Tao, Fulu, Rötter, Reimund, Palosuo, Taru, Hernández Díaz-Ambrona, Carlos Gregorio, Minguez Tudela, Maria Ines, Semenov, Mikhail A., Kersebaum, K.C., Nendel, Claas, Specka, Xenia, Hoffmann, Holger, Ewert, Frank, Dambreville, Anaelle, Martre, Pierre, Rodríguez Fernández, Lucía, Ruiz Ramos, Margarita, Gaiser, Thomas, Höhn, Jukka, Salo, Tapio, Ferrise, Roberto, Bindi, Marco, Cammarano, Davide, Schulman, Alan H, Tao, Fulu, Rötter, Reimund, Palosuo, Taru, Hernández Díaz-Ambrona, Carlos Gregorio, Minguez Tudela, Maria Ines, Semenov, Mikhail A., Kersebaum, K.C., Nendel, Claas, Specka, Xenia, Hoffmann, Holger, Ewert, Frank, Dambreville, Anaelle, Martre, Pierre, Rodríguez Fernández, Lucía, Ruiz Ramos, Margarita, Gaiser, Thomas, Höhn, Jukka, Salo, Tapio, Ferrise, Roberto, Bindi, Marco, Cammarano, Davide, and Schulman, Alan H
Abstract: Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was 4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive informati
Published: 2018

22. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

Author: Fronzek, S., Pirtioja, Nina, Carter, Timothy, Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., 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, P., Jacquemin, I., Kersebaum, K.C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez Sánchez, Alfredo, Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., Röter, R.P., Fronzek, S., Pirtioja, Nina, Carter, Timothy, Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., 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, P., Jacquemin, I., Kersebaum, K.C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez Sánchez, Alfredo, Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., and Röter, R.P.
Abstract: 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 th
Published: 2018

23. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Author: Tao, Fulu, Rötter, Reimund, Palosuo, Taru, Hernández Díaz-Ambrona, Carlos Gregorio, Minguez Tudela, Maria Ines, Semenov, Mikhail A., Kersebaum, K.C., Nendel, Claas, Specka, Xenia, Hoffmann, Holger, Ewert, Frank, Dambreville, Anaelle, Martre, Pierre, Rodríguez Fernández, Lucía, Ruiz Ramos, Margarita, Gaiser, Thomas, Höhn, Jukka, Salo, Tapio, Ferrise, Roberto, Bindi, Marco, Cammarano, Davide, Schulman, Alan H, Tao, Fulu, Rötter, Reimund, Palosuo, Taru, Hernández Díaz-Ambrona, Carlos Gregorio, Minguez Tudela, Maria Ines, Semenov, Mikhail A., Kersebaum, K.C., Nendel, Claas, Specka, Xenia, Hoffmann, Holger, Ewert, Frank, Dambreville, Anaelle, Martre, Pierre, Rodríguez Fernández, Lucía, Ruiz Ramos, Margarita, Gaiser, Thomas, Höhn, Jukka, Salo, Tapio, Ferrise, Roberto, Bindi, Marco, Cammarano, Davide, and Schulman, Alan H
Abstract: Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was 4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive informati
Published: 2018

24. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

Author: Fronzek, S., Pirtioja, Nina, Carter, Timothy, Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., 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, P., Jacquemin, I., Kersebaum, K.C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez Sánchez, Alfredo, Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., Röter, R.P., Fronzek, S., Pirtioja, Nina, Carter, Timothy, Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., 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, P., Jacquemin, I., Kersebaum, K.C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez Sánchez, Alfredo, Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., and Röter, R.P.
Abstract: 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 th
Published: 2018

25. Simulation of soil organic carbon effects on long-term winter wheat (Triticum aestivum) production under varying fertilizer inputs

Author: Ghaley, Bhim Bahadur, Wosten, Henk, Olesen, Jørgen Eivind, Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Borgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan-Peter, Porter, John Roy, Ghaley, Bhim Bahadur, Wosten, Henk, Olesen, Jørgen Eivind, Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Borgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan-Peter, and Porter, John Roy
Published: 2018

26. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

Author: 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. Ines, Montesino San Martin, 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., 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. Ines, Montesino San Martin, 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, and Rötter, Reimund P.
Published: 2018

27. Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment

Author: Ruiz-Ramos, M., Ferrise, Roberto, Rodríguez, A, Lorite, I. J., Bindi, Marco, Carter, T. R., Fronzek, S, Palosuo, T., Pirttioja, N., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, Bertrand, 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 Pouzols, Federico, Nendel, C., Porter, John Roy, Ruget, F., Semenov, M. A., Steinmetz, Z., Stratonovitch, P., Supit, Iwan, Tao, F., Trnka, M., de Wit, Cynthia A., Rötter, Reimund P, Ruiz-Ramos, M., Ferrise, Roberto, Rodríguez, A, Lorite, I. J., Bindi, Marco, Carter, T. R., Fronzek, S, Palosuo, T., Pirttioja, N., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, Bertrand, 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 Pouzols, Federico, Nendel, C., Porter, John Roy, Ruget, F., Semenov, M. A., Steinmetz, Z., Stratonovitch, P., Supit, Iwan, Tao, F., Trnka, M., de Wit, Cynthia A., and Rötter, Reimund P
Abstract: Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socio-economic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the "According to Our Current Knowledge" (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T), [CO2] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO2] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO2] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based o
Published: 2018

28. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

Author: 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., 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, and Rötter, Reimund P.
Abstract: 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
Published: 2018

29. Simulation of soil organic carbon effects on long-term winter wheat (Triticum aestivum) production under varying fertilizer inputs

Author: Ghaley, Bhim B., Wösten, Henk, Olesen, Jørgen E., Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Børgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan Peter, Porter, John R., Ghaley, Bhim B., Wösten, Henk, Olesen, Jørgen E., Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Børgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan Peter, and Porter, John R.
Abstract: Soil organic carbon (SOC) has a vital role to enhance agricultural productivity and for mitigation of climate change. To quantify SOC effects on productivity, process models serve as a robust tool to keep track of multiple plant and soil factors and their interactions affecting SOC dynamics. We used soil-plant-atmospheric model viz. DAISY, to assess effects of SOC on nitrogen (N) supply and plant available water (PAW) under varying N fertilizer rates in winter wheat (Triticum aestivum) in Denmark. The study objective was assessment of SOC effects on winter wheat grain and aboveground biomass accumulation at three SOC levels (low: 0.7% SOC; reference: 1.3% SOC; and high: 2% SOC) with five nitrogen rates (0–200 kg N ha−1) and PAW at low, reference, and high SOC levels. The three SOC levels had significant effects on grain yields and aboveground biomass accumulation at only 0–100 kg N ha−1 and the SOC effects decreased with increasing N rates until no effects at 150–200 kg N ha−1. PAW had significant positive correlation with SOC content, with high SOC retaining higher PAW compared to low and reference SOC. The mean PAW and SOC correlation was given by PAW% = 1.0073 × SOC% + 15.641. For the 0.7–2% SOC range, the PAW increase was small with no significant effects on grain yields and aboveground biomass accumulation. The higher winter wheat grain and aboveground biomass was attributed to higher N supply in N deficient wheat production system. Our study suggested that building SOC enhances agronomic productivity at only 0–100 kg N ha−1. Maintenance of SOC stock will require regular replenishment of SOC, to compensate for the mineralization process degrading SOC over time. Hence, management can maximize realization of SOC benefits by building up SOC and maintaining N rates in the range 0–100 kg N ha−1, to reduce the off-farm N losses depending on the environmental zones, land use and the production system.
Published: 2018

30. Simulation of soil organic carbon effects on long-term winter wheat (Triticum aestivum) production under varying fertilizer inputs

Author: Ghaley, Bhim Bahadur, Wosten, Henk, Olesen, Jørgen Eivind, Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Borgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan-Peter, Porter, John Roy, Ghaley, Bhim Bahadur, Wosten, Henk, Olesen, Jørgen Eivind, Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Borgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan-Peter, and Porter, John Roy
Published: 2018

31. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Author: Brilli, Lorenzo, Bechini, Luca, Bindi, Marco, Carozzi, Marco, Cavalli, Daniele, Conant, Richard, Dorich, Cristopher, Doro, Luca, Ehrhardt, Fiona, Farina, Roberta, Ferrise, Roberto, Fitton, Nuala, Francaviglia, Rosa, Grace, Peter, other, and, Brilli, Lorenzo, Bechini, Luca, Bindi, Marco, Carozzi, Marco, Cavalli, Daniele, Conant, Richard, Dorich, Cristopher, Doro, Luca, Ehrhardt, Fiona, Farina, Roberta, Ferrise, Roberto, Fitton, Nuala, Francaviglia, Rosa, Grace, Peter, and other, and
Published: 2017

32. Communicating soil carbon science to farmers:incorporating credibility, salience and legitimacy

Author: Ingram, Julie, Mills, Jane, Dibari, Camilla, Ferrise, Roberto, Ghaley, Bhim Bahadur, Hansen, Jens Grønbech, Iglesias, Ana, Karaczun, Zbigniew, McVittie, Alistair, Merante, Paolo, Molnar, Andras, Sánchez, Berta, Ingram, Julie, Mills, Jane, Dibari, Camilla, Ferrise, Roberto, Ghaley, Bhim Bahadur, Hansen, Jens Grønbech, Iglesias, Ana, Karaczun, Zbigniew, McVittie, Alistair, Merante, Paolo, Molnar, Andras, and Sánchez, Berta
Abstract: A key narrative within climate change science is that conserving and improving soil carbon through agricultural practices can contribute to agricultural productivity and is a promising option for mitigating carbon loss through sequestration. This paper examines the potential disconnect between science and practice in the context of communicating information about soil carbon management. It focuses on the information producing process and on stakeholder (adviser, farmer representative, policy maker etc) assessment of the attributes credibility, salience and legitimacy. In doing this it draws on results from consultations with stakeholders in the SmartSOIL project which aimed to provide decision support guidelines about practices that optimise carbon mitigation and crop productivity. An iterative methodology, used to engage stakeholders in developing, testing and validating a range of decision support guidelines in six case study regions across Europe, is described. This process enhanced legitimacy and revealed the importance, and the different dimensions, of stakeholder views on credibility and salience. The results also highlight the complexities and contested nature of managing soil carbon. Some insights are gained into how to achieve more effective communication about soil carbon management, including the need to provide opportunities in projects and research programmes for dialogue to engender better understanding between science and practice.
Published: 2016

33. Assessing uncertainties of water footprints using an ensemble of crop growth models on winter wheat

Author: Kersebaum, Kurt Christian, Kroes, Joop, Gobin, Anne, Takáč, Jozef, Hlavinka, Petr, Trnka, Miroslav, Ventrella, Domenico, Giglio, Luisa, Ferrise, Roberto, Moriondo, Marco, Marta, Anna Dalla, Luo, Qunying, Eitzinger, Josef, Mirschel, Wilfried, Weigel, Hans Joachim, Manderscheid, Remy, Hoffmann, Munir, Nejedlik, Pavol, Iqbal, Muhammad Anjum, Hösch, Johannes, Kersebaum, Kurt Christian, Kroes, Joop, Gobin, Anne, Takáč, Jozef, Hlavinka, Petr, Trnka, Miroslav, Ventrella, Domenico, Giglio, Luisa, Ferrise, Roberto, Moriondo, Marco, Marta, Anna Dalla, Luo, Qunying, Eitzinger, Josef, Mirschel, Wilfried, Weigel, Hans Joachim, Manderscheid, Remy, Hoffmann, Munir, Nejedlik, Pavol, Iqbal, Muhammad Anjum, and Hösch, Johannes
Abstract: Crop productivity and water consumption form the basis to calculate the water footprint (WF) of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%-19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%-39%. Models responded differently to elevated CO2 in a FACE (Free-Air Carbon Dioxide Enrichment) experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%-49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%-68% of the total actual evapotranspiration.
Published: 2016

34. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

Author: Pirttioja, Nina, Carter, T. R., Fronzek, S., Bindi, Marco, Hoffmann, H., Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., Acutis, Marco, Asseng, Senthold, Baranowsk, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, Fraçois, F., Gaiser, Thomas, Hlavinka, P., Jacquemin, I., Kersebaum, K. C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez, A., Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., Rötter, Reimund, Pirttioja, Nina, Carter, T. R., Fronzek, S., Bindi, Marco, Hoffmann, H., Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., Acutis, Marco, Asseng, Senthold, Baranowsk, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, Fraçois, F., Gaiser, Thomas, Hlavinka, P., Jacquemin, I., Kersebaum, K. C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez, A., Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., and Rötter, Reimund
Abstract: This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.
Published: 2015

35. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

Author: Pirttioja, Nina, Carter, T. R., Fronzek, S., Bindi, Marco, Hoffmann, H., Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., Acutis, Marco, Asseng, Senthold, Baranowsk, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, Fraçois, F., Gaiser, Thomas, Hlavinka, P., Jacquemin, I., Kersebaum, K. C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez, A., Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., Rötter, Reimund, Pirttioja, Nina, Carter, T. R., Fronzek, S., Bindi, Marco, Hoffmann, H., Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., Acutis, Marco, Asseng, Senthold, Baranowsk, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, Fraçois, F., Gaiser, Thomas, Hlavinka, P., Jacquemin, I., Kersebaum, K. C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez, A., Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., and Rötter, Reimund
Abstract: This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.
Published: 2015

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35 results on '"Ferrise, Roberto"'

1. Proposal and extensive test of a calibration protocol for crop phenology models

2. AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat

3. A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

4. Data from the AgMIP-Wheat high-yielding traits experiment for modeling potential production of wheat: field experiments and multi-model simulations

5. Data from the AgMIP-Wheat high-yielding traits experiment for modeling potential production of wheat: field experiments and multi-model simulations

6. Evidence for increasing global wheat yield potential

7. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

8. Evidence for increasing global wheat yield potential

9. Data from the winter wheat potential yield experiment in New Zealand and response to variable sowing dates and densities: field experiments and AgMIP-Wheat multi-model simulations

10. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

11. Data from the winter wheat potential yield experiment in New Zealand and response to variable sowing dates and densities: field experiments and AgMIP-Wheat multi-model simulations

12. Methodology to assess the changing risk of yield failure due to heat and drought stress under climate change

13. Pan-European multi-crop model ensemble simulations of wheat and grain maize under climate change scenarios

14. Climate change impact and adaptation for wheat protein

15. Climate change impact and adaptation for wheat protein

16. Global wheat production with 1.5 and 2.0°C above pre-industrial warming

17. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

18. Climate change impact and adaptation for wheat protein

19. Climate change impact and adaptation for wheat protein

20. Global wheat production with 1.5 and 2.0°C above pre-industrial warming

21. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

22. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

23. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

24. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

25. Simulation of soil organic carbon effects on long-term winter wheat (Triticum aestivum) production under varying fertilizer inputs

26. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

27. Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment

28. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

29. Simulation of soil organic carbon effects on long-term winter wheat (Triticum aestivum) production under varying fertilizer inputs

30. Simulation of soil organic carbon effects on long-term winter wheat (Triticum aestivum) production under varying fertilizer inputs

31. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

32. Communicating soil carbon science to farmers:incorporating credibility, salience and legitimacy

33. Assessing uncertainties of water footprints using an ensemble of crop growth models on winter wheat

34. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

35. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

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