Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-3905-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-13-3905-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
01 Sep 2020
Development and technical paper |
| 01 Sep 2020
| 01 Sep 2020
The importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillage
Potsdam Institute for Climate Impact Research (PIK), member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Wageningen University, Soil Geography and Landscape Group, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Stephen Del Grosso
USDA-ARS, Soil Management and Sugar Beet Research Unit, 2150 Centre Ave. Bldg. D, Fort Collins, CO 80526, USA
Stephen Ogle
NREL, Colorado State University, Fort Collins, CO 80523, USA
Stephen Williams
NREL, Colorado State University, Fort Collins, CO 80523, USA
Sara Minoli
Potsdam Institute for Climate Impact Research (PIK), member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Susanne Rolinski
Potsdam Institute for Climate Impact Research (PIK), member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Jens Heinke
Potsdam Institute for Climate Impact Research (PIK), member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Jetse J. Stoorvogel
Wageningen University, Soil Geography and Landscape Group, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Christoph Müller
Potsdam Institute for Climate Impact Research (PIK), member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Related authors
No articles found.
Lily-belle Sweet, Christoph Müller, Jonas Jägermeyr, and Jakob Zscheischler
EGUsphere, https://doi.org/10.5194/egusphere-2025-3006, https://doi.org/10.5194/egusphere-2025-3006, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study presents a method to identify climate drivers of an impact, such as agricultural yield failure, from high-resolution weather data. The approach systematically generates, selects and combines predictors that generalise across different environments. Tested on crop model simulations, the identified drivers are used to create parsimonious models that achieve high predictive performance over long time horizons, offering a more interpretable alternative to black-box models.
Edna Johanna Molina Bacca, Miodrag Stevanović, Benjamin Leon Bodirsky, Jonathan Cornelis Doelman, Louise Parsons Chini, Jan Volkholz, Katja Frieler, Christopher Paul Oliver Reyer, George Hurtt, Florian Humpenöder, Kristine Karstens, Jens Heinke, Christoph Müller, Jan Philipp Dietrich, Hermann Lotze-Campen, Elke Stehfest, and Alexander Popp
Earth Syst. Dynam., 16, 753–801, https://doi.org/10.5194/esd-16-753-2025, https://doi.org/10.5194/esd-16-753-2025, 2025
Short summary
Short summary
Land-use change projections are vital for impact studies. This study compares updated land-use model projections, including CO2 fertilization among other upgrades, from the MAgPIE and IMAGE models under three scenarios, highlighting differences, uncertainty hotspots, and harmonization effects. Key findings include reduced bioenergy crop demand projections and differences in grassland area allocation and sizes, with socioeconomic–climate scenarios' largest effect on variance starting in 2030.
Marie Brunel, Stephen Wirth, Markus Drüke, Kirsten Thonicke, Henrique Barbosa, Jens Heinke, and Susanne Rolinski
EGUsphere, https://doi.org/10.5194/egusphere-2025-922, https://doi.org/10.5194/egusphere-2025-922, 2025
Short summary
Short summary
Farmers often use fire to clear dead pasture biomass, impacting vegetation and soil nutrients. This study integrates fire management into a DGVM to assess its effects, focusing on Brazil. The results show that combining grazing and fire management reduces vegetation carbon and soil nitrogen over time. The research highlights the need to include these practices in models to improve pasture management assessments and calls for better data on fire usage and its long-term effects.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Elena Xoplaki, Florian Ellsäßer, Jens Grieger, Katrin M. Nissen, Joaquim G. Pinto, Markus Augenstein, Ting-Chen Chen, Hendrik Feldmann, Petra Friederichs, Daniel Gliksman, Laura Goulier, Karsten Haustein, Jens Heinke, Lisa Jach, Florian Knutzen, Stefan Kollet, Jürg Luterbacher, Niklas Luther, Susanna Mohr, Christoph Mudersbach, Christoph Müller, Efi Rousi, Felix Simon, Laura Suarez-Gutierrez, Svenja Szemkus, Sara M. Vallejo-Bernal, Odysseas Vlachopoulos, and Frederik Wolf
Nat. Hazards Earth Syst. Sci., 25, 541–564, https://doi.org/10.5194/nhess-25-541-2025, https://doi.org/10.5194/nhess-25-541-2025, 2025
Short summary
Short summary
Europe frequently experiences compound events, with major impacts. We investigate these events’ interactions, characteristics, and changes over time, focusing on socio-economic impacts in Germany and central Europe. Highlighting 2018’s extreme events, this study reveals impacts on water, agriculture, and forests and stresses the need for impact-focused definitions and better future risk quantification to support adaptation planning.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Felix Jäger, Jonas Schwaab, Yann Quilcaille, Michael Windisch, Jonathan Doelman, Stefan Frank, Mykola Gusti, Petr Havlik, Florian Humpenöder, Andrey Lessa Derci Augustynczik, Christoph Müller, Kanishka Balu Narayan, Ryan Sebastian Padrón, Alexander Popp, Detlef van Vuuren, Michael Wögerer, and Sonia Isabelle Seneviratne
Earth Syst. Dynam., 15, 1055–1071, https://doi.org/10.5194/esd-15-1055-2024, https://doi.org/10.5194/esd-15-1055-2024, 2024
Short summary
Short summary
Climate change mitigation strategies developed with socioeconomic models rely on the widespread (re)planting of trees to limit global warming below 2°. However, most of these models neglect climate-driven shifts in forest damage like fires. By assessing existing mitigation scenarios, we show the exposure of projected forestation areas to fire-promoting weather conditions. Our study highlights the problem of ignoring climate-driven shifts in forest damage and ways to address it.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
Short summary
Short summary
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
Short summary
Short summary
We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Kai Kornhuber, Harald Kunstmann, Joaquim G. Pinto, Kirsten Warrach-Sagi, and Elena Xoplaki
Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, https://doi.org/10.5194/nhess-23-1699-2023, 2023
Short summary
Short summary
The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favorable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming.
Kristine Karstens, Benjamin Leon Bodirsky, Jan Philipp Dietrich, Marta Dondini, Jens Heinke, Matthias Kuhnert, Christoph Müller, Susanne Rolinski, Pete Smith, Isabelle Weindl, Hermann Lotze-Campen, and Alexander Popp
Biogeosciences, 19, 5125–5149, https://doi.org/10.5194/bg-19-5125-2022, https://doi.org/10.5194/bg-19-5125-2022, 2022
Short summary
Short summary
Soil organic carbon (SOC) has been depleted by anthropogenic land cover change and agricultural management. While SOC models often simulate detailed biochemical processes, the management decisions are still little investigated at the global scale. We estimate that soils have lost around 26 GtC relative to a counterfactual natural state in 1975. Yet, since 1975, SOC has been increasing again by 4 GtC due to a higher productivity, recycling of crop residues and manure, and no-tillage practices.
Vera Porwollik, Susanne Rolinski, Jens Heinke, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Biogeosciences, 19, 957–977, https://doi.org/10.5194/bg-19-957-2022, https://doi.org/10.5194/bg-19-957-2022, 2022
Short summary
Short summary
The study assesses impacts of grass cover crop cultivation on cropland during main-crop off-season periods applying the global vegetation model LPJmL (V.5.0-tillage-cc). Compared to simulated bare-soil fallowing practices, cover crops led to increased soil carbon content and reduced nitrogen leaching rates on the majority of global cropland. Yield responses of main crops following cover crops vary with location, duration of altered management, crop type, water regime, and tillage practice.
Tobias Herzfeld, Jens Heinke, Susanne Rolinski, and Christoph Müller
Earth Syst. Dynam., 12, 1037–1055, https://doi.org/10.5194/esd-12-1037-2021, https://doi.org/10.5194/esd-12-1037-2021, 2021
Short summary
Short summary
Soil organic carbon sequestration on cropland has been proposed as a climate change mitigation strategy. We simulate different agricultural management practices under climate change scenarios using a global biophysical model. We find that at the global aggregated level, agricultural management practices are not capable of enhancing total carbon storage in the soil, yet for some climate regions, we find that there is potential to enhance the carbon content in cropland soils.
Yao Zhang, Jocelyn M. Lavallee, Andy D. Robertson, Rebecca Even, Stephen M. Ogle, Keith Paustian, and M. Francesca Cotrufo
Biogeosciences, 18, 3147–3171, https://doi.org/10.5194/bg-18-3147-2021, https://doi.org/10.5194/bg-18-3147-2021, 2021
Short summary
Short summary
Soil organic matter (SOM) is essential for the health of soils, and the accumulation of SOM helps removal of CO2 from the atmosphere. Here we present the result of the continued development of a mathematical model that simulates SOM and its measurable fractions. In this study, we simulated several grassland sites in the US, and the model generally captured the carbon and nitrogen amounts in SOM and their distribution between the measurable fractions throughout the entire soil profile.
Yvonne Jans, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Hydrol. Earth Syst. Sci., 25, 2027–2044, https://doi.org/10.5194/hess-25-2027-2021, https://doi.org/10.5194/hess-25-2027-2021, 2021
Short summary
Short summary
Growth of and irrigation water demand on cotton may be challenged by future climate change. To analyze the global cotton production and irrigation water consumption under spatially varying present and future climatic conditions, we use the global terrestrial biosphere model LPJmL. Our simulation results suggest that the beneficial effects of elevated [CO2] on cotton yields overcompensate yield losses from direct climate change impacts, i.e., without the beneficial effect of [CO2] fertilization.
Bruno Ringeval, Christoph Müller, Thomas A. M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, and Sylvain Pellerin
Geosci. Model Dev., 14, 1639–1656, https://doi.org/10.5194/gmd-14-1639-2021, https://doi.org/10.5194/gmd-14-1639-2021, 2021
Short summary
Short summary
We assess how and why global gridded crop models (GGCMs) differ in their simulation of potential yield. We build a GCCM emulator based on generic formalism and fit its parameters against aboveground biomass and yield at harvest simulated by eight GGCMs. Despite huge differences between GGCMs, we show that the calibration of a few key parameters allows the emulator to reproduce the GGCM simulations. Our simple but mechanistic model could help to improve the global simulation of potential yield.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Abigail Snyder, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Karina Williams, Ziwei Wang, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 3995–4018, https://doi.org/10.5194/gmd-13-3995-2020, https://doi.org/10.5194/gmd-13-3995-2020, 2020
Short summary
Short summary
Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work, we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
Cited articles
Alvarez, C., Costantini, A., Alvarez, C. R., Alves, B. J., Jantalia, C. P.,
Martellotto, E. E., and Urquiaga, S.: Soil nitrous oxide emissions under
different management practices in the semiarid region of the Argentinian
Pampas, Nutr. Cycl. Agroecosyst., 94, 209–220, https://doi.org/10.1007/s10705-012-9534-9, 2012. a
Álvaro-Fuentes, J., Morell, F. J., Plaza-Bonilla, D., Arrúe, J. L., and Cantero-Martínez, C.: Modelling tillage and nitrogen fertilization
effects on soil organic carbon dynamics, Soil Till. Res., 120, 32–39,
https://doi.org/10.1016/j.still.2012.01.009, 2012. a
Barton, L., Wolf, B., Rowlings, D., Scheer, C., Kiese, R., Grace, P.,
Stefanova, K., and Butterbach-Bahl, K.: Sampling frequency affects estimates
of annual nitrous oxide fluxes, Sci. Rep., 5, 1–9, https://doi.org/10.1038/srep15912, 2015. a
Begum, K., Kuhnert, M., Yeluripati, J. B., Ogle, S. M., Parton, W. J.,
Williams, S. A., Pan, G., Cheng, K., Ali, M. A., and Smith, P.: Modelling
greenhouse gas emissions and mitigation potentials in fertilized paddy rice
fields in Bangladesh, Geoderma, 341, 206–215, https://doi.org/10.1016/j.geoderma.2019.01.047, 2019. a
Bessou, C., Mary, B., Léonard, J., Roussel, M., Gréhan, E., and
Gabrielle, B.: Modelling soil compaction impacts on nitrous oxide emissions
in arable fields, Eur. J. Soil Sci., 61, 348–363, https://doi.org/10.1111/j.1365-2389.2010.01243.x, 2010. a
Boeckx, P., Van Nieuland, K., and Van Cleemput, O.: Short-term effect of
tillage intensity on N2O and CO2 emissions, Agron. Sustain. Dev., 31, 453–461, https://doi.org/10.1007/s13593-011-0001-9, 2011. a
Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R., and
Zechmeister-Boltenstern, S.: Nitrous oxide emissions from soils: how well do
we understand the processes and their controls?, Philos. T. Roy. Soc. B, 368, 20130122, https://doi.org/10.1098/rstb.2013.0122, 2013. a, b
Campbell, E. E., Johnson, J. M., Jin, V. L., Lehman, R. M., Osborne, S. L.,
Varvel, G. E., and Paustian, K.: Assessing the soil carbon, biomass production, and nitrous oxide emission impact of corn stover management for
bioenergy feedstock production using DAYCENT, Bioenergy Res., 7, 491–502, https://doi.org/10.1007/s12155-014-9414-z, 2014. a, b
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., and Heimann, M.: Carbon and other
biogeochemical cycles, Cambridge University Press, Cambridge, 465–570, 2014. a
Del Grosso, S., Parton, W., Mosier, A., Ojima, D., Kulmala, A., and Phongpan,
S.: General model for N2O and N2 gas emissions from soils due to denitrification, Global Biogeochem. Cy., 14, 1045–1060,
https://doi.org/10.1029/1999GB001225, 2000. a, b
Del Grosso, S., Ojima, D., Parton, W., Mosier, A., Peterson, G., and Schimel,
D.: Simulated effects of dryland cropping intensification on soil organic
matter and greenhouse gas exchanges using the DAYCENT ecosystem model,
Environ. Pollut., 116, S75–S83, https://doi.org/10.1016/S0269-7491(01)00260-3, 2002. a, b
Del Grosso, S., Halvorson, A., and Parton, W.: Testing DAYCENT model
simulations of corn yields and nitrous oxide emissions in irrigated tillage
systems in Colorado, J. Environ. Qual., 37, 1383–1389,
https://doi.org/10.2134/jeq2007.0292, 2008a. a
Del Grosso, S., Parton, W., Ojima, D., Keough, C., Riley, T., and Mosier, A.:
Chapter 18. DAYCENT Simulated Effects of Land Use and Climate on County Level
N Loss Vectors in the USA,Academic Press/Elsevier, Amsterdam, Boston, 1–28, 2008b. a
Del Grosso, S., Ogle, S., Parton, W., and Breidt, F.: Estimating uncertainty in N2O emissions from US cropland soils, Global Biogeochem. Cy., 24, GB1009, https://doi.org/10.1029/2009GB003544, 2010. a
Del Grosso, S. J., Parton, W. J., Mosier, A. R., Ojima, D. S., Kulmala, A. E., and Phongpan, S.: General model for N2O and N2 gas
emissions from soils due to dentrification, Global Biogeochem. Cy., 14,
1045–1060, https://doi.org/10.1029/1999gb001225, 2000. a
Del Grosso, S. J., Ojima, D. S., Parton, W. J., Stehfest, E., Heistemann, M.,
DeAngelo, B., and Rose, S.: Global scale DAYCENT model analysis of greenhouse gas emissions and mitigation strategies for cropped soils, Global Planet. Change, 67, 44–50, https://doi.org/10.1016/j.gloplacha.2008.12.006, 2009. a, b, c, d, e
Deng, Q., Hui, D., Wang, J., Yu, C.-L., Li, C., Reddy, K. C., and Dennis, S.:
Assessing the impacts of tillage and fertilization management on nitrous
oxide emissions in a cornfield using the DNDC model, J. Geophys. Res.-Biogeo., 121, 337–349, https://doi.org/10.1002/2015jg003239, 2016. a, b
Elliott, J., Müller, C., Deryng, D., Chryssanthacopoulos, J., Boote, K. J., Büchner, M., Foster, I., Glotter, M., Heinke, J., Iizumi, T., Izaurralde, R. C., Mueller, N. D., Ray, D. K., Rosenzweig, C., Ruane, A. C., and Sheffield, J.: The Global Gridded Crop Model Intercomparison: data
and modeling protocols for Phase 1 (v1.0), Geosci. Model Dev., 8, 261–277,
https://doi.org/10.5194/gmd-8-261-2015, 2015. a
Erb, K.-H., Luyssaert, S., Meyfroidt, P., Pongratz, J., Don, A., Kloster, S.,
Kuemmerle, T., Fetzel, T., Fuchs, R., Herold, M., Haberl, H., Jones, C. D., Marín-Spiotta, E., McCallum, I., Robertson, E., Seufert, V., Fritz, S., Valade, A., Wiltshire, A., and Dolman, A. J.: Land management: data availability and process understanding for global change studies, Global Change Biol., 23, 512–533, https://doi.org/10.1111/gcb.13443, 2017. a
FAO: World reference base for soil resources, in: vol. 3, Food & Agriculture Org., Rome, 1998. a
Fatichi, S., Or, D., Walko, R., Vereecken, H., Young, M. H., Ghezzehei, T. A., Hengl, T., Kollet, S., Agam, N., and Avissar, R.: Soil structure is an
important omission in Earth System Models, Nat. Commun., 11, 1–11,
https://doi.org/10.1038/s41467-020-14411-z, 2020. a
Fitton, N., Datta, A., Hastings, A., Kuhnert, M., Topp, C., Cloy, J., Rees, R., Cardenas, L., Williams, J., Smith, K., Chadwick, D., and Smith, P.: The challenge of modelling nitrogen management at the field scale: simulation and sensitivity analysis of N2O fluxes across nine experimental sites using DailyDayCent, Environ. Res. Lett., 9, 095003, https://doi.org/10.1088/1748-9326/9/9/095003, 2014. a
Folberth, C., Elliott, J., Müller, C., Balkovic, J., Chryssanthacopoulos,
J., Izaurralde, R. C., Jones, C. D., Khabarov, N., Liu, W., Reddy, A., Schmid, E., Skalský, R., Yang, H., Arneth, H., Ciais, P., Deryng, D., Lawrence, P. J., Olin, S., Pugh, T. A. M., Ruane, A. C., and Wang, X.: Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble, PLoS One, 14, e0221862, https://doi.org/10.1371/journal.pone.0221862, 2019. a
Grandy, A. S., Loecke, T. D., Parr, S., and Robertson, G. P.: Long-term trends in nitrous oxide emissions, soil nitrogen, and crop yields of till and
no-till cropping systems, J. Environ. Qual., 35, 1487–1495,
https://doi.org/10.2134/jeq2005.0166, 2006. a, b
Gregory, J. M.: Soil cover prediction with various amounts and types of crop
residue, T. ASAE, 25, 1333–1337, https://doi.org/10.13031/2013.33723, 1982. a, b
Gryze, S. D., Wolf, A., Kaffka, S. R., Mitchell, J., Rolston, D. E., Temple,
S. R., Lee, J., and Six, J.: Simulating greenhouse gas budgets of four
California cropping systems under conventional and alternative management,
Ecol. Appl., 20, 1805–1819, https://doi.org/10.1890/09-0772.1, 2010. a
Halvorson, A. D., Mosier, A. R., Reule, C. A., and Bausch, W. C.: Nitrogen
and tillage effects on irrigated continuous corn yields, Agron. J., 98, 63–71, https://doi.org/10.2134/agronj2005.0174, 2006. a, b
Hartman, M., Parton, W., Del Grosso, S., Easter, M., Hendryx, J., Hilinski, T., Kelly, R., Keough, C., Killian, K., Lutz, S., Marx, E., McKeown, R., Ogle, S., Ojima, D., Paustian, K., Swan, A., and Williams, S.: The Daily Century Ecosystem, Soil Organic Matter, Nutrient Cycling, Nitrogen Trace Gas, and Methane Model: User Manual, Scientific Basis, and Technical Documentation., Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, 2018. a
Heinke, J., Müller, C., Lannerstad, M., Gerten, D., and Lucht, W.: Freshwater resources under success and failure of the Paris climate agreement, Earth Syst. Dynam., 10, 205–217, https://doi.org/10.5194/esd-10-205-2019, 2019. a
Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S., Kummu, M., and Lucht, W.: Water savings potentials of irrigation systems: global simulation of processes and linkages, Hydrol. Earth Syst. Sci., 19, 3073–3091,
https://doi.org/10.5194/hess-19-3073-2015, 2015. a
Jin, V. L., Schmer, M. R., Stewart, C. E., Sindelar, A. J., Varvel, G. E., and Wienhold, B. J.: Long-term no-till and stover retention each decrease the
global warming potential of irrigated continuous corn, Global Change Biol.,
23, 2848–2862, https://doi.org/10.1111/gcb.13637, 2017. a, b, c
Kelly, R., Parton, W., Hartman, M., Stretch, L., Ojima, D., and Schimel, D.:
Intra‐annual and interannual variability of ecosystem processes in shortgrass steppe, J. Geophys. Res.-Atmos., 105, 20093–20100, https://doi.org/10.1029/2000JD900259, 2000. a
Lutz, F., Herzfeld, T., Heinke, J., Rolinski, S., Schaphoff, S., von Bloh, W., Stoorvogel, J. J., and Müller, C.: Simulating the effect of tillage
practices with the global ecosystem model LPJmL (version 5.0-tillage), Geosci. Model Dev., 12, 2419–2440, https://doi.org/10.5194/gmd-12-2419-2019, 2019a. a, b, c, d, e, f, g, h, i
Lutz, F., Müller, C., Heinke, J., Minoli, S., and Rolinski, S.: LPJmL5.0-tillage: Original source code as used in Lutz et al., 2019:
submitted to Geosci. Model Dev., https://doi.org/10.5281/zenodo.3592381, 2019b. a
Lutz, F., Stoorvogel, J. J., and Müller, C.: Options to model the effects
of tillage on N2O emissions at the global scale, Ecol. Model., 392,
212–225, https://doi.org/10.1016/j.ecolmodel.2018.11.015, 2019c. a
Mei, K., Wang, Z., Huang, H., Zhang, C., Shang, X., Dahlgren, R. A., Zhang, M., and Xia, F.: Stimulation of N2O emission by conservation tillage management in agricultural lands: A meta-analysis, Soil Till. Res., 182, 86–93, https://doi.org/10.1016/j.still.2018.05.006, 2018. a
Mosquera, J., ter Beek, C., and Hol, J.: Precise soil management as a tool to
reduce CH4 and N2O emissions from agricultural soil. II. Field measurements at arable soils in the Netherlands, Report 9067549851, Agrotechnology & Food Innovations, Animal Sciences Group Report No. 28, Wageningen, the Netherlands, 2005. a
Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., and
Foley, J. A.: Closing yield gaps through nutrient and water management, Nature, 490, 254–257, https://doi.org/10.1038/nature11420, 2012. a, b
Necpálová, M., Anex, R. P., Fienen, M. N., Del Grosso, S. J., Castellano, M. J., Sawyer, J. E., Iqbal, J., Pantoja, J. L., and Barker, D. W.: Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling, Environ. Model. Softw., 66, 110–130, https://doi.org/10.1016/j.envsoft.2014.12.011, 2015. a
Oorts, K., Merckx, R., Gréhan, E., Labreuche, J., and Nicolardot, B.:
Determinants of annual fluxes of CO2 and N2O in long-term
no-tillage and conventional tillage systems in northern France, Soil Till. Res., 95, 133–148, https://doi.org/10.1016/j.still.2006.12.002, 2007. a, b, c
Pannkuk, C., Stockle, C., and Papendick, R.: Evaluating CropSyst simulations
of wheat management in a wheat-fallow region of the US pacific northwest,
Agric. Syst., 57, 121–134, https://doi.org/10.1016/s0308-521x(97)00076-0, 1998. a
Parton, W., Mosier, A., Ojima, D., Valentine, D., Schimel, D., Weier, K., and
Kulmala, A. E.: Generalized model for N2 and N2O production from nitrification and denitrification, Global Biogeochem. Cy., 10, 401–412, https://doi.org/10.1029/96GB01455, 1996. a, b
Parton, W., Holland, E., Del Grosso, S., Hartman, M., Martin, R., Mosier, A.,
Ojima, D., and Schimel, D.: Generalized model for NOx and N2O emissions from soils, J. Geophys. Res.-Atmos., 106, 17403–17419, https://doi.org/10.1029/2001JD900101, 2001. a, b, c
Parton, W. J., Hartman, M., Ojima, D., and Schimel, D.: DAYCENT and its land
surface submodel: description and testing, Global Planet. Change, 19, 35–48,
https://doi.org/10.1016/s0921-8181(98)00040-x, 1998. a
Plaza-Bonilla, D., Álvaro Fuentes, J., Bareche, J., Pareja-Sánchez, E., Justes, É., and Cantero-Martínez, C.: No-tillage reduces long-term yield-scaled soil nitrous oxide emissions in rainfed Mediterranean
agroecosystems: A field and modelling approach, Agr. Ecosyst. Environ., 262, 36–47, https://doi.org/10.1016/j.agee.2018.04.007, 2018. a
Potter, P., Ramankutty, N., Bennett, E. M., and Donner, S. D.: Characterizing
the spatial patterns of global fertilizer application and manure production,
Earth Interact., 14, 1–22, https://doi.org/10.1175/2009EI288.1, 2010. a, b
Rolinski, S., Müller, C., Heinke, J., Weindl, I., Biewald, A., Bodirsky, B. L., Bondeau, A., Boons-Prins, E. R., Bouwman, A. F., Leffelaar, P. A., te Roller, J. A., Schaphoff, S., and Thonicke, K.: Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6, Geosci. Model Dev., 11, 429–451, https://doi.org/10.5194/gmd-11-429-2018, 2018. a
Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P., Antle, J. M., Nelson, G. C., Porter, C., Janssen, S., Asseng, S., Basso, B., Ewert, F., Wallach, D., Baigorria, G., and Winter, J. M.: The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies, Agr. Forest. Meteorol., 170, 166–182, https://doi.org/10.1016/j.agrformet.2012.09.011, 2013. a
Saxton, K., Rawls, W., Romberger, J., and Papendick, R.: Estimating generalized soil-water characteristics from texture, Soil Sci. Soc. Am. J., 50, 1031–1036, https://doi.org/10.2136/sssaj1986.03615995005000040039x, 1986. a
Saxton, K. E. and Rawls, W. J.: Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions, Soil Sci. Soc. Am. J., 70, 1569–1577, https://doi.org/10.2136/sssaj2005.0117, 2006.
a
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J., Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description, Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, 2018. a, b
Schlüter, S., Großmann, C., Diel, J., Wu, G.-M., Tischer, S., Deubel, A., and Rücknagel, J.: Long-term effects of conventional and reduced tillage on soil structure, soil ecological and soil hydraulic properties, Geoderma, 332, 10–19, https://doi.org/10.1016/j.geoderma.2018.07.001, 2018. a
Smith, J. and Smith, P.: Environmental modelling: an introduction, Oxford
University Press, Oxford, 2007. a
Smith, K.: Changing views of nitrous oxide emissions from agricultural soil:
key controlling processes and assessment at different spatial scales, Eur. J. Soil Sci., 68, 137–155, https://doi.org/10.1111/ejss.12409, 2017. a
Snyder, C. S., Bruulsema, T. W., Jensen, T. L., and Fixen, P. E.: Review of
greenhouse gas emissions from crop production systems and fertilizer management effects, Agr. Ecosyst. Environ., 133, 247–266,
https://doi.org/10.1016/j.agee.2009.04.021, 2009. a
Van Kessel, C., Venterea, R., Six, J., Adviento-Borbe, M. A., Linquist, B.,
and van Groenigen, K. J.: Climate, duration, and N placement determine
N2O emissions in reduced tillage systems: a meta-analysis, Global
Change Biol., 19, 33–44, https://doi.org/10.1111/j.1365-2486.2012.02779.x, 2013. a, b, c
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U., Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G., Tóth, B., Verhoef, A., Vanderborght, J., van der Ploeg, M. J., Weihermüller, L., Zacharias, S., Zhang, Y., and Vereecken, H.: Pedotransfer functions in Earth system science: Challenges and perspectives, Rev. Geophys., 55, 1199–1256, https://doi.org/10.1002/2017RG000581, 2017. a
Venterea, R. T., Maharjan, B., and Dolan, M. S.: Fertilizer source and tillage effects on yield-scaled nitrous oxide emissions in a corn cropping system, J. Environ. Qual., 40, 1521–1531, https://doi.org/10.2134/jeq2011.0039, 2011. a
Waha, K., Van Bussel, L., Müller, C., and Bondeau, A.: Climate-driven
simulation of global crop sowing dates, Global Ecol. Biogeogr., 21, 247–259,
https://doi.org/10.1111/j.1466-8238.2011.00678.x, 2012. a, b
Yang, Q., Zhang, X., Abraha, M., Del Grosso, S., Robertson, G., and Chen, J.:
Enhancing the soil and water assessment tool model for simulating N2O emissions of three agricultural systems, Ecosyst. Health Sustain., 3, e01259, https://doi.org/10.1002/ehs2.1259, 2017. a, b
Yoo, J., Woo, S.-H., Park, K.-D., and Chung, K.-Y.: Effect of no-tillage and
conventional tillage practices on the nitrous oxide (N2O) emissions in an upland soil: soil N2O emission as affected by the fertilizer applications, Appl. Biol. Chem., 59, 787–797, https://doi.org/10.1007/s13765-016-0226-z, 2016. a
Short summary
Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analyses on global estimates of tillage effects on N2O emissions. By comparing model results with observational data of four experimental sites and outputs from field-scale DayCent model simulations, we show that advancing information on agricultural management, as well as the representation of soil moisture dynamics, improves LPJmL5.0-tillage and the estimates of tillage effects on N2O emissions.
Previous findings have shown deviations between the LPJmL5.0-tillage model and results from...
Special issue