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3. Spatial and temporal uncertainty of crop yield aggregations

Author

Porwollik, Vera, Müller, Christoph, Elliott, Joshua, Chryssanthacopoulos, James, Iizumi, Toshichika, Ray, Deepak K., Ruane, Alex C., Arneth, Almut, Balkovič, Juraj, Ciais, Philippe, Deryng, Delphine, Folberth, Christian, Izaurralde, Roberto C., Jones, Curtis D., Khabarov, Nikolay, Lawrence, Peter J., Liu, Wenfeng, Pugh, Thomas A.M., Reddy, Ashwan, Sakurai, Gen, Schmid, Erwin, Wang, Xuhui, de Wit, Allard, and Wu, Xiuchen

Published
2017
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5. AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat

Author

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

Published
2023
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6. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

Author

Müller, Christoph, Elliott, Joshua, Kelly, David, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Folberth, Christian, Hoek, Steven, Izaurralde, Roberto C., Jones, Curtis D., Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A. M., Reddy, Ashwan, Rosenzweig, Cynthia, Ruane, Alex C., Sakurai, Gen, Schmid, Erwin, Skalsky, Rastislav, Wang, Xuhui, de Wit, Allard, and Yang, Hong

Published
2019
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7. Parameterization-Induced Uncertainties and Impacts of Crop Management Harmonization in a Global Gridded Crop Model Ensemble

Author

Folberth, Christian, Elliott, Joshua, Muller, Christoph, Balkovic, Juraj, Chryssanthacopoulos, James, Izaurralde, Roberto C, Jones, Curtis D, Khabarov, Nikolay, Liu, Wenfeng, Reddy, Ashwan, Schmid, Erwin, Skalsky, Rastislav, Yang, Hong, Arneth, Almut, Ciais, Philippe, Deryng, Delphine, Lawrence, Peter J, Olin, Stefan, Pugh, Thomas A. M, Ruane, Alex C, and Wang, Xuhui

Subjects
Life Sciences (General), Earth Resources And Remote Sensing
Abstract

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Published
2019
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8. Climate Change Impact and Adaptation for Wheat Protein

Author

Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Rötter, 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, Sanctis, Giacomo De, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. César, Jabloun, Mohamed, Jones, Curtis D, Kassie, Belay T, Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, Martin, Manuel Montesino San, Müller, 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, Velde, Marijn Van der, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank

Subjects
Meteorology And Climatology
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
2018
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9. Global Patterns of Crop Yield Stability Under Additional Nutrient and Water Inputs

Author

Mueller, Christoph, Elliott, Joshua, Pugh, Thomas A. M, Ruane, Alex C, Ciais, Philippe, Balkovic, Juraj, Deryng, Delphine, Folberth, Christian, Izaurralde, R. Cesar, Jones, Curtis D, Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Reddy, Ashwan D, Schmid, Erwin, and Wang, Xuhui

Subjects
Meteorology And Climatology
Abstract

Agricultural production must increase to feed a growing and wealthier population, as well as to satisfy increasing demands for biomaterials and biomass-based energy. At the same time, deforestation and land-use change need to be minimized in order to preserve biodiversity and maintain carbon stores in vegetation and soils. Consequently, agricultural land use needs to be intensified in order to increase food production per unit area of land. Here we use simulations of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 to assess implications of input-driven intensification (water, nutrients) on crop yield and yield stability, which is an important aspect in food security. We find region- and crop-specific responses for the simulated period 1980+/-2009 with broadly increasing yield variability under additional nitrogen inputs and stabilizing yields under additional water inputs (irrigation), reflecting current patterns of water and nutrient limitation. The different models of the GGCMI ensemble show similar response patterns, but model differences warrant further research on management assumptions, such as variety selection and soil management, and inputs as well as on model implementation of different soil and plant processes, such as on heat stress, and parameters. Higher variability in crop productivity under higher fertilizer input will require adequate buffer mechanisms in trade and distribution/storage networks to avoid food price volatility.

Published
2018
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11. The Uncertainty of Crop Yield Projections Is Reduced by Improved Temperature Response Functions

Author

Wang, Enli, Martre, Pierre, Zhao, Zhigan, Ewert, Frank, Maiorano, Andrea, Rotter, Reimund P, Kimball, Bruce A, Ottman, Michael J, White, Jeffrey W, Reynolds, Matthew P, Alderman, Phillip D, Aggarwal, Pramod K, Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J, De Sanctis, Giacomo, Doltra, Jordi, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A, Izaurralde, Roberto C, Jabloun, Mohamed, Jones, Curtis D, Kersebaum, Kurt C, Koehler, Ann-Kristin, Liu, Leilei, Muller, Christoph, Naresh Kumar, Soora, Nendel, Claas, O'Leary, Garry, Oleson, Jorgen E, Palosuo, Tara, Priesack, Eckhart, Eyshi, Rezaei, Ehsan, Ripoche, Dominique, Ruane, Alex C, Semenov, Mikhail A, Scherbak, Lurii, Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, and Asseng, Senthold

Subjects
Meteorology And Climatology
Abstract

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for is greater than 50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 C to 33 C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Published
2017
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12. The International Heat Stress Genotype Experiment for Modeling Wheat Response to Heat: Field Experiments and AgMIP-Wheat Multi-Model Simulations

Author

Martre, Pierre, Reynolds, Matthew P, Asseng, Senthold, Ewert, Frank, Alderman, Phillip D, Cammarano, Davide, Maiorano, Andrea, Ruane, Alexander C, Aggarwal, Pramod K, Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Challinor, Andrew J, De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hohenheim, Gerrit, Hunt, Leslie A, Izaurralde, Roberto C, Jabloun, Mohamed, Jones, Curtis D, Kassie, Belay T, Kersebaum, Kurt T, Koehler, Ann-Kristin, Mueller, Christoph, Kumar, Soora Naresh, Liu, Bing, Lobell, David B, Nendel, Claas, O’Leary, Garry, Olesen, Jørgen E, Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Roetter, Reimund P, Semenov, Mikhail A, Stoeckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wang, Enli, White, Jeffrey W, Wolf, Joost, Zhao, Zhigan, and Zhu, Yan

Subjects
Meteorology And Climatology
Abstract

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.

Published
2017
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13. Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change

Author

Franke, James A., primary, Müller, Christoph, additional, Minoli, Sara, additional, Elliott, Joshua, additional, Folberth, Christian, additional, Gardner, Charles, additional, Hank, Tobias, additional, Izaurralde, Roberto Cesar, additional, Jägermeyr, Jonas, additional, Jones, Curtis D., additional, Liu, Wenfeng, additional, Olin, Stefan, additional, Pugh, Thomas A.M., additional, Ruane, Alex C., additional, Stephens, Haynes, additional, Zabel, Florian, additional, and Moyer, Elisabeth J., additional

Published
2021
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14. Assessing and reducing the environmental impact of dairy production systems in the northern US in a changing climate

Author

Veltman, Karin, primary, Rotz, C. Alan, additional, Chase, Larry, additional, Cooper, Joyce, additional, Forest, Chris E., additional, Ingraham, Peter A., additional, Izaurralde, R. César, additional, Jones, Curtis D., additional, Nicholas, Robert E., additional, Ruark, Matthew D., additional, Salas, William, additional, Thoma, Greg, additional, and Jolliet, Olivier, additional

Published
2021
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15. Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions

Author

Wang, Enli, Martre, Pierre, Zhao, Zhigan, Ewert, Frank, Maiorano, Andrea, Rötter, Reimund P., Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jeffrey W., Reynolds, Matthew P., Alderman, Phillip D., Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kersebaum, Kurt C., Koehler, Ann-Kristin, Liu, Leilei, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O’Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Ruane, Alex C., Semenov, Mikhail A., Shcherbak, Iurii, Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, and Asseng, Senthold

Published
2017
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16. Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change.

Author

Franke, James A., Müller, Christoph, Minoli, Sara, Elliott, Joshua, Folberth, Christian, Gardner, Charles, Hank, Tobias, Izaurralde, Roberto Cesar, Jägermeyr, Jonas, Jones, Curtis D., Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A.M., Ruane, Alex C., Stephens, Haynes, Zabel, Florian, and Moyer, Elisabeth J.

Subjects
WINTER wheat, CLIMATE change, RICE, WHEAT, GROWING season, FARMERS, FOOD production
Abstract

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate‐related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process‐based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no‐adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing‐zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies. [ABSTRACT FROM AUTHOR]

Published
2022
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17. 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, and Asseng, Senthold

Subjects
1.5°C warming, climate change, extreme low yields, food security, model ensemble, wheat production
Published
2019

18. Analysis of beneficial management practices to mitigate environmental impacts in dairy production systems around the Great Lakes

Author

Kim, Daesoo, primary, Stoddart, Nick, additional, Rotz, C. Alan, additional, Veltman, Karin, additional, Chase, Larry, additional, Cooper, Joyce, additional, Ingraham, Pete, additional, Izaurralde, R. César, additional, Jones, Curtis D., additional, Gaillard, Richard, additional, Aguirre-Villegas, Horacio A., additional, Larson, Rebecca A., additional, Ruark, Matt, additional, Salas, William, additional, Jolliet, Olivier, additional, and Thoma, Gregory J., additional

Published
2019
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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

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

21. 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

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

Author

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

Published
2019
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23. A quantitative assessment of Beneficial Management Practices to reduce carbon and reactive nitrogen footprints and phosphorus losses on dairy farms in the US Great Lakes region

Author

Veltman, Karin, primary, Rotz, C. Alan, additional, Chase, Larry, additional, Cooper, Joyce, additional, Ingraham, Pete, additional, Izaurralde, R. César, additional, Jones, Curtis D., additional, Gaillard, Richard, additional, Larson, Rebecca A., additional, Ruark, Matt, additional, Salas, William, additional, Thoma, Greg, additional, and Jolliet, Olivier, additional

Published
2018
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24. Multimodel ensembles improve predictions of crop–environment–management interactions

Author

Wallach, Daniel, primary, Martre, Pierre, additional, Liu, Bing, additional, Asseng, Senthold, additional, Ewert, Frank, additional, Thorburn, Peter J., additional, van Ittersum, Martin, additional, Aggarwal, Pramod K., additional, Ahmed, Mukhtar, additional, Basso, Bruno, additional, Biernath, Christian, additional, Cammarano, Davide, additional, Challinor, Andrew J., additional, De Sanctis, Giacomo, additional, Dumont, Benjamin, additional, Eyshi Rezaei, Ehsan, additional, Fereres, Elias, additional, Fitzgerald, Glenn J., additional, Gao, Y., additional, Garcia‐Vila, Margarita, additional, Gayler, Sebastian, additional, Girousse, Christine, additional, Hoogenboom, Gerrit, additional, Horan, Heidi, additional, Izaurralde, Roberto C., additional, Jones, Curtis D., additional, Kassie, Belay T., additional, Kersebaum, Kurt C., additional, Klein, Christian, additional, Koehler, Ann‐Kristin, additional, Maiorano, Andrea, additional, Minoli, Sara, additional, Müller, Christoph, additional, Naresh Kumar, Soora, additional, Nendel, Claas, additional, O'Leary, Garry J., additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rötter, Reimund P., additional, Semenov, Mikhail A., additional, Stöckle, Claudio, additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Wolf, Joost, additional, and Zhang, Zhao, additional

Published
2018
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26. Global patterns of crop yield stability under additional nutrient and water inputs

Author

Müller, Christoph, primary, Elliott, Joshua, additional, Pugh, Thomas A. M., additional, Ruane, Alex C., additional, Ciais, Philippe, additional, Balkovic, Juraj, additional, Deryng, Delphine, additional, Folberth, Christian, additional, Izaurralde, R. Cesar, additional, Jones, Curtis D., additional, Khabarov, Nikolay, additional, Lawrence, Peter, additional, Liu, Wenfeng, additional, Reddy, Ashwan D., additional, Schmid, Erwin, additional, and Wang, Xuhui, additional

Published
2018
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29. The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations

Author

Martre, Pierre, Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jeffrey W., Asseng, Senthold, Ewert, Frank, Cammarano, Davide, Maiorano, Andrea, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Koehler, Ann-Kristin, Müller, Christoph, Kumar, Soora Naresh, Liu, Bing, Lobell, David B., Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wang, Enli, Wolf, Joost, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jeffrey W., Asseng, Senthold, Ewert, Frank, Cammarano, Davide, Maiorano, Andrea, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Koehler, Ann-Kristin, Müller, Christoph, Kumar, Soora Naresh, Liu, Bing, Lobell, David B., Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wang, Enli, Wolf, Joost, Zhao, Zhigan, and Zhu, Yan

Abstract

The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.

Published
2018

30. Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions

Author

Wang, Enli, primary, Martre, Pierre, additional, Zhao, Zhigan, additional, Ewert, Frank, additional, Maiorano, Andrea, additional, Rötter, Reimund P., additional, Kimball, Bruce A., additional, Ottman, Michael J., additional, Wall, Gerard W., additional, White, Jeffrey W., additional, Reynolds, Matthew P., additional, Alderman, Phillip D., additional, Aggarwal, Pramod K., additional, Anothai, Jakarat, additional, Basso, Bruno, additional, Biernath, Christian, additional, Cammarano, Davide, additional, Challinor, Andrew J., additional, De Sanctis, Giacomo, additional, Doltra, Jordi, additional, Fereres, Elias, additional, Garcia-Vila, Margarita, additional, Gayler, Sebastian, additional, Hoogenboom, Gerrit, additional, Hunt, Leslie A., additional, Izaurralde, Roberto C., additional, Jabloun, Mohamed, additional, Jones, Curtis D., additional, Kersebaum, Kurt C., additional, Koehler, Ann-Kristin, additional, Liu, Leilei, additional, Müller, Christoph, additional, Kumar, Soora Naresh, additional, Nendel, Claas, additional, O’Leary, Garry, additional, Olesen, Jørgen E., additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Rezaei, Ehsan Eyshi, additional, Ripoche, Dominique, additional, Ruane, Alex C., additional, Semenov, Mikhail A., additional, Shcherbak, Iurii, additional, Stöckle, Claudio, additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Thorburn, Peter, additional, Waha, Katharina, additional, Wallach, Daniel, additional, Wang, Zhimin, additional, Wolf, Joost, additional, Zhu, Yan, additional, and Asseng, Senthold, additional

Published
2017
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31. The uncertainty of crop yield projections is reduced by improved temperature response functions

Author

Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), Wang, Enli, Martre, Pierre, Zhao, Zhigan, Ewert, Frank, Maiorano, Andrea, Rötter, Reimund P., Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jefrrey W., Reynolds, Matthew, Alderman, Phillip, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres Castiel, Elías, García Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kersebaum, Kurt C., Koehler, Ann-Kristin, Liu, Leilei, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Ruane, Alexander C., Semenov, Mikhail A., Shcherbak, Iurii, Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, Asseng, Senthold, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), Wang, Enli, Martre, Pierre, Zhao, Zhigan, Ewert, Frank, Maiorano, Andrea, Rötter, Reimund P., Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jefrrey W., Reynolds, Matthew, Alderman, Phillip, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres Castiel, Elías, García Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kersebaum, Kurt C., Koehler, Ann-Kristin, Liu, Leilei, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Ruane, Alexander C., Semenov, Mikhail A., Shcherbak, Iurii, Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, and Asseng, Senthold

Abstract

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Published
2017

32. Supplementary material to "Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates"

Author

Folberth, Christian, primary, Elliott, Joshua, additional, Müller, Christoph, additional, Balkovic, Juraj, additional, Chryssanthacopoulos, James, additional, Izaurralde, Roberto C., additional, Jones, Curtis D., additional, Khabarov, Nikolay, additional, Liu, Wenfeng, additional, Reddy, Ashwan, additional, Schmid, Erwin, additional, Skalský, Rastislav, additional, Yang, Hong, additional, Arneth, Almut, additional, Ciais, Philippe, additional, Deryng, Delphine, additional, Lawrence, Peter J., additional, Olin, Stefan, additional, Pugh, Thomas A. M., additional, Ruane, Alex C., additional, and Wang, Xuhui, additional

Published
2016
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33. Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates

Author

Folberth, Christian, primary, Elliott, Joshua, additional, Müller, Christoph, additional, Balkovic, Juraj, additional, Chryssanthacopoulos, James, additional, Izaurralde, Roberto C., additional, Jones, Curtis D., additional, Khabarov, Nikolay, additional, Liu, Wenfeng, additional, Reddy, Ashwan, additional, Schmid, Erwin, additional, Skalský, Rastislav, additional, Yang, Hong, additional, Arneth, Almut, additional, Ciais, Philippe, additional, Deryng, Delphine, additional, Lawrence, Peter J., additional, Olin, Stefan, additional, Pugh, Thomas A. M., additional, Ruane, Alex C., additional, and Wang, Xuhui, additional

Published
2016
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34. Similar estimates of temperature impacts on global wheat yield by three independent methods

Author

Liu, Bing, primary, Asseng, Senthold, additional, Müller, Christoph, additional, Ewert, Frank, additional, Elliott, Joshua, additional, Lobell, David B., additional, Martre, Pierre, additional, Ruane, Alex C., additional, Wallach, Daniel, additional, Jones, James W., additional, Rosenzweig, Cynthia, additional, Aggarwal, Pramod K., additional, Alderman, Phillip D., additional, Anothai, Jakarat, additional, Basso, Bruno, additional, Biernath, Christian, additional, Cammarano, Davide, additional, Challinor, Andy, additional, Deryng, Delphine, additional, Sanctis, Giacomo De, additional, Doltra, Jordi, additional, Fereres, Elias, additional, Folberth, Christian, additional, Garcia-Vila, Margarita, additional, Gayler, Sebastian, additional, Hoogenboom, Gerrit, additional, Hunt, Leslie A., additional, Izaurralde, Roberto C., additional, Jabloun, Mohamed, additional, Jones, Curtis D., additional, Kersebaum, Kurt C., additional, Kimball, Bruce A., additional, Koehler, Ann-Kristin, additional, Kumar, Soora Naresh, additional, Nendel, Claas, additional, O’Leary, Garry J., additional, Olesen, Jørgen E., additional, Ottman, Michael J., additional, Palosuo, Taru, additional, Prasad, P. V. Vara, additional, Priesack, Eckart, additional, Pugh, Thomas A. M., additional, Reynolds, Matthew, additional, Rezaei, Ehsan E., additional, Rötter, Reimund P., additional, Schmid, Erwin, additional, Semenov, Mikhail A., additional, Shcherbak, Iurii, additional, Stehfest, Elke, additional, Stöckle, Claudio O., additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Thorburn, Peter, additional, Waha, Katharina, additional, Wall, Gerard W., additional, Wang, Enli, additional, White, Jeffrey W., additional, Wolf, Joost, additional, Zhao, Zhigan, additional, and Zhu, Yan, additional

Published
2016
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35. Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC models.

Author

Gaillard, Richard K., Jones, Curtis D., Ingraham, Pete, Collier, Sarah, Izaurralde, Roberto Cesar, Jokela, William, Osterholz, William, Salas, William, Vadas, Peter, and Ruark, Matthew D.

Subjects
AGRICULTURAL ecology, NITROUS oxide & the environment, FARMS, CROPPING systems, NITROGEN fertilizers, SOIL temperature, DENITRIFICATION
Abstract

Abstract: Process‐based models are increasingly used to study agroecosystem interactions and N2O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N2O flux from three process‐based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi‐model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross‐model bias to underestimate high magnitude daily and cumulative N2O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH4+, and soil NO3−, which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N2O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development. [ABSTRACT FROM AUTHOR]

Published
2018
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36. Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates.

Author

Folberth, Christian, Elliott, Joshua, Müller, Christoph, Balkovic, Juraj, Chryssanthacopoulos, James, Izaurralde, Roberto C., Jones, Curtis D., Khabarov, Nikolay, Wenfeng Liu, Reddy, Ashwan, Schmid, Erwin, Skalský, Rastislav, Hong Yang, Arneth, Almut, Ciais, Philippe, Deryng, Delphine, Lawrence, Peter J., Olin, Stefan, Pugh, Thomas A. M., and Ruane, Alex C.

Subjects
CROP management, CROP yields, CULTIVARS, AGRONOMY, EVAPOTRANSPIRATION
Abstract

Global gridded crop models (GGCMs) combine field-scale agronomic models or sets of plant growth algorithms with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different bio-physical models, setups, and input data. While algorithms have been in the focus of recent GGCM comparisons, this study investigates differences in maize and wheat yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison (GGCMI) project. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, geographic distribution of cultivars, and selection of subroutines e.g. for the estimation of potential evapotranspiration or soil erosion. The analyses reveal long-term trends and inter-annual yield variability in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. Absolute yield levels as well depend not only on nutrient supply but also on the parameterization and distribution of crop cultivars. All GGCMs show an intermediate performance in reproducing reported absolute yield levels or inter-annual dynamics. Our findings suggest that studies focusing on the evaluation of differences in bio-physical routines may require further harmonization of input data and management assumptions in order to eliminate background noise resulting from differences in model setups. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for bracketing such uncertainties as long as comprehensive global datasets taking into account regional differences in crop management, cultivar distributions and coefficients for parameterizing agro-environmental processes are lacking. Finally, we recommend improvements in the documentation of setups and input data of GGCMs in order to allow for sound interpretability, comparability and reproducibility of published results. [ABSTRACT FROM AUTHOR]

Published
2016
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38. Global patterns of crop yield stability under additional nutrient and water inputs

Author

Müller, Christoph, Elliott, Joshua W., Pugh, Thomas A. M., Ruane, Alexander, Ciais, Philippe, Balkovic, Juraj, Deryng, Delphine, Folberth, Christian, Izaurralde, R. Cesar, Jones, Curtis D., Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Reddy, Ashwan D., Schmid, Erwin, and Wang, Xuhui

Subjects
2. Zero hunger, 13. Climate action, Agricultural productivity, 15. Life on land, Land capability for agriculture, Crop yields--Computer simulation, Crops and climate
Abstract

Agricultural production must increase to feed a growing and wealthier population, as well as to satisfy increasing demands for biomaterials and biomass-based energy. At the same time, deforestation and land-use change need to be minimized in order to preserve biodiversity and maintain carbon stores in vegetation and soils. Consequently, agricultural land use needs to be intensified in order to increase food production per unit area of land. Here we use simulations of AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) phase 1 to assess implications of input-driven intensification (water, nutrients) on crop yield and yield stability, which is an important aspect in food security. We find region- and crop-specific responses for the simulated period 1980–2009 with broadly increasing yield variability under additional nitrogen inputs and stabilizing yields under additional water inputs (irrigation), reflecting current patterns of water and nutrient limitation. The different models of the GGCMI ensemble show similar response patterns, but model differences warrant further research on management assumptions, such as variety selection and soil management, and inputs as well as on model implementation of different soil and plant processes, such as on heat stress, and parameters. Higher variability in crop productivity under higher fertilizer input will require adequate buffer mechanisms in trade and distribution/storage networks to avoid food price volatility.

39. Similar estimates of temperature impacts on global wheat yield by three independent methods (vol 6, pg 1130, 2016)

Author

Liu, Bing, Asseng, Senthold, Muller, Christoph, Ewert, Frank, Elliott, Joshua, Lobell, David B., Martre, Pierre, Ruane, Alex C., Wallach, Daniel, Jones, James W., Rosenzweig, Cynthia, Aggarwal, Pramod K., Alderman, Phillip D., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andy, Deryng, Delphine, Sanctis, Giacomo, Doltra, Jordi, Fereres, Elias, Folberth, Christian, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kersebaum, Kurt C., Kimball, Bruce A., Koehler, Ann-Kristin, Kumar, Soora Naresh, Nendel, Claas, O Leary, Garry J., Olesen, Jorgen E., Ottman, Michael J., Palosuo, Taru, Prasad, P. V. Vara, Priesack, Eckart, Pugh, Thomas A. M., Reynolds, Matthew, Rezaei, Ehsan E., Rtter, Reimund P., Schmid, Erwin, Mikhail Semenov, Shcherbak, Iurii, Stehfest, Elke, Stockle, Claudio O., Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wall, Gerard W., Wang, Enli, White, Jeffrey W., Wolf, Joost, Zhao, Zhigan, and Zhu, Yan

40. Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble

Author

Folberth, Christian, Elliott, Joshua, Müller, Christoph, Balkovič, Juraj, Chryssanthacopoulos, James, Izaurralde, Roberto C., Jones, Curtis D., Khabarov, Nikolay, Liu, Wenfeng, Reddy, Ashwan, Schmid, Erwin, Skalský, Rastislav, Yang, Hong, Arneth, Almut, Ciais, Philippe, Deryng, Delphine, Lawrence, Peter J., Olin, Stefan, Pugh, Thomas A. M., Ruane, Alex C., and Wang, Xuhui

Subjects
2. Zero hunger, 13. Climate action, 15. Life on land

41. Mid-season nitrogen management for winter wheat under price and weather uncertainty.

Author

Chen, Xiangjie, Chambers, Robert G., Bandaru, Varaprasad, Jones, Curtis D., Ochsner, Tyson E., Nandan, Rohit, Irigireddy, Bharath C., Lollato, Romulo P., Witt, Travis W., and Rice, Charles W.

Subjects
*FARMERS' attitudes, *PRICES, *EXPECTED returns, *AGRICULTURAL meteorology, *WEATHER forecasting, *WINTER wheat
Abstract

In-season nitrogen (N) management tools are essential for optimizing N application rates, maximizing farmers' economic returns and minimizing adverse environmental impacts. The primary limitation to developing such tools is the risk associated with uncertainties in weather forecasts and crop price projections required to estimate yields and returns for different N rates. Therefore, characterizing the risk associated with these uncertainties is crucial for determining optimum N rates in-season. This study investigated the N application decision-making process for farmers, accounting for risks associated with weather and crop price uncertainties through crop modeling and economic analysis. We used field trial data for winter wheat in Kansas to examine how optimal nitrogen rates and economic returns vary over sites, years, and differing farmers' risk attitudes. First, the Environmental Policy Integrated Climate (EPIC) agroecosystem model was used to simulate the distribution of final yields under different N applications during early spring. Then, an autoregressive moving average (ARMA) model estimated the wheat price distribution at harvest based on historical prices. Finally, optimal N application rates for farmers with different risk appetites were estimated using two risk decision models: the constant-absolute-risk-averse (CARA) expected utility model, which treats upside (higher-than-expected returns) and downside (lower-than-expected returns) deviations equally, and the invariant-preference, generalized-deviation (IPGD) model, which focuses on downside risk. We found that optimal N rates vary greatly between sites and years, as well as across farmers with different risk preferences. Due to the positive skewness of economic return distribution, farmers tend to apply lower N rates when considering downside risk. On average, the optimal N rate for farmers with a CARA coefficient of 0.002 is 77 kg/ha in the CARA model and 67 kg/ha in the IPGD model. Compared to the outcome of risk-neutral N usage, risk-averse N usage for a farmer with a CARA coefficient of 0.008 could reduce the uncertainty (standard deviation) of return by 6.2 %, on average, while the expected return decreased by only 1.2 %. By lowering the N rate, risk-averse farmers would reduce the uncertainty of returns and incur a minor return loss, suggesting the possibility of improving agricultural resilience while also improving N use efficiency. Our analysis also underscores the importance of yearly site-specific N management, given the substantial variation in optimal rates across years and locations. This study provides the foundation for an N application decision framework that considers both weather and price uncertainty. The analysis also demonstrates the potential co-benefit of enhancing agriculture's climate and market resilience while potentially lowering N losses. • A nitrogen decision framework simultaneously considers the price and weather uncertainty. • Optimal nitrogen rates vary greatly among farmers with different risk preferences. • Farmers tend to apply lower nitrogen rates when considering downside risk. • Lowering the nitrogen rate could enhance agriculture resilience while reducing nitrogen losses. [ABSTRACT FROM AUTHOR]

Published
2024
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