Articles | Volume 11, issue 2
https://doi.org/10.5194/essd-11-823-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-11-823-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
12 Jun 2019
| 12 Jun 2019
Generating a rule-based global gridded tillage dataset
Vera Porwollik
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research, Member of the Leibniz
Association, Potsdam 14412, Germany
Susanne Rolinski
Potsdam Institute for Climate Impact Research, Member of the Leibniz
Association, Potsdam 14412, Germany
Jens Heinke
Potsdam Institute for Climate Impact Research, Member of the Leibniz
Association, Potsdam 14412, Germany
Christoph Müller
Potsdam Institute for Climate Impact Research, Member of the Leibniz
Association, Potsdam 14412, Germany
Related authors
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.
Sibyll Schaphoff, David Hötten, Christoph Müller, Dieter Gerten, Sebastian Ostberg, and Werner von Bloh
Geosci. Model Dev., 19, 7615–7651, https://doi.org/10.5194/gmd-19-7615-2026, https://doi.org/10.5194/gmd-19-7615-2026, 2026
Short summary
Short summary
Methane is a powerful driver of climate change, yet emissions from wetlands and farming remain uncertain. We improved a global vegetation model that links water, plants and soils to methane production and release. The model now captures waterlogged areas, methane creation and escape, and flood-tolerant plants. It reproduces global wetland patterns and emissions more realistically, helping assess how climate and land-use change may affect future methane release and improve climate projections.
Jannes Breier, Luana Schwarz, Hannah Prawitz, Werner von Bloh, Christoph Müller, Stephen Björn Wirth, Max Bechthold, Dieter Gerten, and Jonathan F. Donges
Geosci. Model Dev., 19, 6829–6855, https://doi.org/10.5194/gmd-19-6829-2026, https://doi.org/10.5194/gmd-19-6829-2026, 2026
Short summary
Short summary
We present a new modelling framework that combines simulations of environmental change and human decision-making in an integrated approach. It allows researchers to explore how farming decisions affect land, water, and climate, and how these environmental changes influence future decisions. By connecting people and nature, it helps investigate food security, climate adaptation, and sustainability challenges, and supports the development of more realistic scenarios of future environmental change.
Lily-belle Sweet, Christoph Müller, Jonas Jägermeyr, and Jakob Zscheischler
Geosci. Model Dev., 19, 6687–6735, https://doi.org/10.5194/gmd-19-6687-2026, https://doi.org/10.5194/gmd-19-6687-2026, 2026
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.
Xinrui Liu, Thomas Gasser, Jianmin Ma, Junfeng Liu, Jonas Jägermeyr, Christoph Müller, Christian Folberth, Florian Zabel, Atul K. Jain, Wenfeng Liu, and Heidi Webber
Geosci. Model Dev., 19, 5857–5880, https://doi.org/10.5194/gmd-19-5857-2026, https://doi.org/10.5194/gmd-19-5857-2026, 2026
Short summary
Short summary
This paper presents a crop yield emulator for four major crops (maize, rice, soybean, and wheat). Trained on process-based crop model simulations, it captures yield responses to key drivers: atmospheric CO2, temperature, water availability, and nitrogen use. The emulator closely reproduces the behavior of complex crop models and aligns well with FAO (Food and Agriculture Organization)-reported historical yields. It provides a robust and efficient method for representing agricultural outcomes in climate change impact assessments.
Heindriken Dahlmann, Lauren S. Andersen, Sibyll Schaphoff, Fabian Stenzel, Johanna Braun, Christoph Müller, and Dieter Gerten
Hydrol. Earth Syst. Sci., 30, 3185–3201, https://doi.org/10.5194/hess-30-3185-2026, https://doi.org/10.5194/hess-30-3185-2026, 2026
Short summary
Short summary
Green water stress can negatively affect agricultural production and is often mitigated through irrigation. In this global modelling study, we investigate where and to what extent the implementation of irrigation helps to compensate for green water stress but at the same time leads to an increase in blue water scarcity. Our findings highlight the need to consider both water stresses together, along with their dynamic interactions, for sustainable water management.
Marie Hemmen, Heidi Webber, Werner von Bloh, Jens Heinke, and Christoph Müller
EGUsphere, https://doi.org/10.5194/egusphere-2026-1898, https://doi.org/10.5194/egusphere-2026-1898, 2026
Short summary
Short summary
In this study we present a lightweight approach to compute crop canopy temperatures in computationally expensive models, which we apply in the global Lund-Potsdam-Jena managed Land model. The evaluation reveals that the developed approach reproduces cooling and heating effects of the canopy for daily maximum temperatures and suggests that replacement of standard 2 m air temperature input by simulated canopy temperatures improves the skill to model high temperature impacts on crop growth.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida Bagus Mandhara Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique M. Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe R. Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido R. van der Werf, Sebastiaan J. van de Velde, Anthony P. Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data, 18, 3211–3288, https://doi.org/10.5194/essd-18-3211-2026, https://doi.org/10.5194/essd-18-3211-2026, 2026
Short summary
Short summary
The Global Carbon Budget 2025 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 (1750–2025). 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.
Susanne Rolinski, Jens Heinke, Stephen B. Wirth, and Christoph Müller
EGUsphere, https://doi.org/10.5194/egusphere-2026-1529, https://doi.org/10.5194/egusphere-2026-1529, 2026
Short summary
Short summary
Livestock rearing in semi-arid steppe regions is traditionally extensive but under pressure both from increasing demand and by climate change. We demonstrate by applying a vegetation model that increasing management intensity by increasing fertilization and animal densities result in increasing livestock productivity but also environmental impacts in a nonlinear way. That allows to find management combinations with low environmental impacts without jeopardizing livestock productivity.
Marie Brunel, Stephen B. Wirth, Markus Drüke, Kirsten Thonicke, Henrique Barbosa, Jens Heinke, and Susanne Rolinski
Biogeosciences, 23, 939–965, https://doi.org/10.5194/bg-23-939-2026, https://doi.org/10.5194/bg-23-939-2026, 2026
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 Dynamic Global Vegetation Model (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.
Luana Schwarz, Jannes Breier, Hannah Prawitz, Max Bechthold, Werner von Bloh, Sara M. Constantino, Dieter Gerten, Jobst Heitzig, Ronja Hotz, Leander John, Christoph Müller, Johan Rockström, and Jonathan F. Donges
EGUsphere, https://doi.org/10.5194/egusphere-2025-4079, https://doi.org/10.5194/egusphere-2025-4079, 2025
Short summary
Short summary
We present a novel global model that links farmer decisions with ecological processes to explore how agricultural systems co-evolve. Unlike previous tools, it captures feedbacks between society and nature at up-to planetary scale. We find that conservation practices can restore soil health and support stable harvests. Adoption spreads through learning and norms, showing how regeneration at the farm scale can ripple outward, contributing to global sustainability and Earth system resilience.
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.
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.
Cited articles
Andales, A. A., Batchelor, W. D., Anderson, C. E., Farnham, D. E., and
Whigham, D. K.: Incorporating tillage effects into a soybean model,
Agr. Syst., 66, 69–98, https://doi.org/10.1016/S0308-521X(00)00037-8, 2000.
Andrews, D. J. and Kumar, K. A.: Pearl Millet for Food, Feed, and Forage,
in: Advances in Agronomy, edited by: Sparks, D. L., Academic Press, 1992.
Bartholomé, E. and Belward, A. S.: GLC2000: a new approach to global
land cover mapping from Earth observation data, Int. J. Remote, 26, 1959–1977, https://doi.org/10.1080/01431160412331291297, 2005.
Basso, B., Ritchie, J., Grace, P., and Sartori, L.: Simulating tillage
impacts on soil biophysical proprerties using the SALUS model, Ital.
J. Agron., 1, 677–688, https://doi.org/10.4081/ija.2006.677, 2006.
Carlson, K. M., Gerber, J. S., Mueller, N. D., Herrero, M., MacDonald, G.
K., Brauman, K. A., Havlik, P., O'Connell, C. S., Johnson, J. A., Saatchi,
S., and West, P. C.: Greenhouse gas emissions intensity of global croplands,
Nat. Clim. Change, 7, 63–68, https://doi.org/10.1038/nclimate3158, 2016.
Case, A.: Neighborhood influence and technological change, Reg. Sci.
Urban Econ., 22, 491–508, https://doi.org/10.1016/0166-0462(92)90041-X, 1992.
CTIC: Tillage Type Definitions, Conservation Technology Information Center,
available at: http://www.ctic.purdue.edu/resourcedisplay/322/, last access:
1 June 2018.
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.
Derpsch, R.: No-tillage and conservation agriculture: A progress report, in:
No-Till Farming Systems, Special Publication No 3, edited by: Goddard, T.,
Zoebisch, M. A., Gan, Y. T., Ellis, W., Watson, A., and Sombatpanit, S.,
World Assocciation of Soil and Water Conservation, Bangkok, 2008.
Derpsch, R., Friedrich, T., Kassam, A., and Hongwen, L.: Current status of
adoption of no-till farming in the world and some of its main benefits,
Int. J. Agr. Biol. Eng., 3, 1–25, https://doi.org/10.3965/j.issn.1934-6344.2010.01.001-025, 2010.
Dixon, J., Gulliver, A., and Gibbon, D.: Farming systems and poverty:
Improving farmers' livelihoods in a changing world, FAO & World Bank,
Rome, Italy & Washington DC, USA, 2001.
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, Glob.
Change Biol., 23, 512–533, https://doi.org/10.1111/gcb.13443, 2016.
EUROSTAT: Agri-environmental indicator – tillage practices, in: Fact sheet, Statistics explained, available at: http://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Survey_on_agricultural_production_methods_(SAPM), last access: 23 August 2018.
Fader, M., von Bloh, W., Shi, S., Bondeau, A., and Cramer, W.: Modelling Mediterranean agro-ecosystems by including agricultural trees
in the LPJmL model, Geosci. Model Dev., 8, 3545–3561, https://doi.org/10.5194/gmd-8-3545-2015, 2015.
FAO: FAO GEONETWORK, Global map of aridity – 10 arc minutes (GeoLayer),
available at: http://www.fao.org/geonetwork/srv/en/main.home?uuid=221072ae-2090-48a1-be6f-5a88f061431a (last access: 24 November 2017),
FAO, Rome, Italy, 2015.
FAO: Conservation Agriculture, AQUASTAT Main Database Food and Agriculture
Organization of the United Nations (FAO), 2016.
Folberth, C., Skalsky, R., Moltchanova, E., Balkovic, J., Azevedo, L. B.,
Obersteiner, M., and van der Velde, M.: Uncertainty in soil data can
outweigh climate impact signals in global crop yield simulations, Nat.
Commun., 7, 11872, https://doi.org/10.1038/ncomms11872, 2016.
Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E.,
Duerauer, M., Albrecht, F., Schill, C., Perger, C., Havlik, P., Mosnier, A.,
Thornton, P., Wood-Sichra, U., Herrero, M., Becker-Reshef, I., Justice, C.,
Hansen, M., Gong, P., Abdel Aziz, S., Cipriani, A., Cumani, R., Cecchi, G.,
Conchedda, G., Ferreira, S., Gomez, A., Haffani, M., Kayitakire, F.,
Malanding, J., Mueller, R., Newby, T., Nonguierma, A., Olusegun, A., Ortner,
S., Rajak, D. R., Rocha, J., Schepaschenko, D., Schepaschenko, M., Terekhov,
A., Tiangwa, A., Vancutsem, C., Vintrou, E., Wenbin, W., van der Velde, M.,
Dunwoody, A., Kraxner, F., and Obersteiner, M.: Mapping global cropland and
field size, Glob. Change Biol., 21, 1980–1992, https://doi.org/10.1111/gcb.12838,
2015.
Giller, K. E., Andersson, J. A., Corbeels, M., Kirkegaard, J., Mortensen,
D., Erenstein, O., and Vanlauwe, B.: Beyond conservation agriculture,
Front. Plant Sci., 6, 870, https://doi.org/10.3389/fpls.2015.00870, 2015.
Global Administrative Areas: GADM Database of global administrative areas
v.2.7. University of California, Davis, California, USA, Rome, Italy,
2015.
Govaerts, B., Verhulst, N., Castellanos-Navarrete, A., Sayre, K. D., Dixon,
J., and Dendooven, L.: Conservation Agriculture and Soil Carbon
Sequestration: Between Myth and Farmer Reality, Crit. Rev. Plant
Sci., 28, 97–122, https://doi.org/10.1080/07352680902776358, 2009.
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G.
B. M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh,
M. G., and Gonzalez, M. R.: SoilGrids1km – Global soil information based on
automated mapping, College of Global Change and Earth System Science,
Beijing Normal University/ISRIC-World Soil Information, PLoS ONE, 9,
e105992, https://doi.org/10.1371/journal.pone.0105992, 2014.
Herrero, M., Thornton, P. K., Power, B., Bogard, J. R., Remans, R., Fritz,
S., Gerber, J. S., Nelson, G., See, L., Waha, K., Watson, R. A., West, P.
C., Samberg, L. H., van de Steeg, J., Stephenson, E., van Wijk, M., and
Havlík, P.: Farming and the geography of nutrient production for human
use: A transdisciplinary analysis, The Lancet Planetary Health, 1, e33–e42,
https://doi.org/10.1016/S2542-5196(17)30007-4, 2017.
Hijmans, R. J. and van Etten, J.: Raster: Geographic analysis and modeling
with raster data, R package version 2.0-12, available at:
http://CRAN.R-project.org/package=raster (last access: 13 July 2018), 2012.
Hirsch, A. L., Wilhelm, M., Davin, E. L., Thiery, W., and Seneviratne, S.
I.: Can climate-effective land management reduce regional warming?, J.
Geophys. Res.-Atmos., 122, 2269–2288,
https://doi.org/10.1002/2016JD026125, 2017.
Hirsch, A. L., Prestele, R., Davin, E. L., Seneviratne, S. I., Thiery, W.,
and Verburg, P. H.: Modelled biophysical impacts of conservation agriculture
on local climates, Glob. Change Biol., 24, 4758–4774,
https://doi.org/10.1111/gcb.14362, 2018.
IFPRI/IIASA: cell5m_allockey_xy.dbf.zip, in:
Global Spatially-Disaggregated Crop Production Statistics Data for 2005
International Food Policy Research Institute and International Institute for
Applied Systems Analysis, Harvard Dataverse, V9, https://doi.org/10.7910/dvn/dhxbjx/lvrjlf, 2017a.
IFPRI/IIASA: spam2005v3r1_global_phys_area.geotiff.zip, in: Global Spatially-Disaggregated
Crop Production Statistics Data for 2005 Version 3.1, International Food
Policy Research Institute and International Institute for Applied Systems
Analysis, Harvard Dataverse, V9, https://doi.org/10.7910/dvn/dhxbjx/k5hvuk,
2017b.
Jodha, N. S.: Resource base as a determinant of cropping patterns, Economics
Dept., ICRISAT, Hyderabad, India, 1977.
Jones, A. D.: On-Farm Crop Species Richness Is Associated with Household
Diet Diversity and Quality in Subsistence- and Market-Oriented Farming
Households in Malawi, J. Nutr., 147, 86–96, https://doi.org/10.3945/jn.116.235879, 2017.
Kassam, A., Friedrich, T., Shaxson, F., and Pretty, J.: The spread of
Conservation Agriculture: justification, sustainability and uptake,
Int. J. Agr. Sustain., 7, 292–320, https://doi.org/10.3763/ijas.2009.0477, 2009.
Kassam, A., Friedrich, T., Derpsch, R., and Kienzle, J.: Overview of the
Worldwide Spread of Conservation Agriculture, Field Actions Science Reports, 8, available at: http://journals.openedition.org/factsreports/3966 (last access: 13 April 2019), 2015.
Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data,
9, 927–953, https://doi.org/10.5194/essd-9-927-2017, 2017.
Kouwenhoven, J. K., Perdok, U. D., Boer, J., and Oomen, G. J. M.: Soil
management by shallow mouldboard ploughing in The Netherlands, Soil
Till. Res., 65, 125–139, https://doi.org/10.1016/S0167-1987(01)00271-9, 2002.
Levin, G.: Farm size and landscape composition in relation to landscape
changes in Denmark, Geogr. Tidsskr., 106,
45–59, https://doi.org/10.1080/00167223.2006.10649556, 2006.
Levis, S., Hartman, M. D., and Bonan, G. B.: The Community Land Model underestimates
land-use CO2 emissions by neglecting soil disturbance from cultivation, Geosci. Model Dev.,
7, 613–620, https://doi.org/10.5194/gmd-7-613-2014, 2014.
Lobell, D. B., Bala, G., and Duffy, P. B.: Biogeophysical impacts of
cropland management changes on climate, Geophys. Res. Lett., 33, L06708,
https://doi.org/10.1029/2005GL025492, 2006.
Lowder, S. K., Skoet, J., and Singh, S.: What do we really know about the
number and distribution of farms and family farms worldwide? Background
paper for The State of Food and Agriculture 2014, Rome, FAO, 2014.
Lowder, S. K., Skoet, J., and Raney, T.: The Number, Size, and Distribution
of Farms, Smallholder Farms, and Family Farms Worldwide, World Development,
87, 16–29, https://doi.org/10.1016/j.worlddev.2015.10.041, 2016.
Maertens, A. and Barrett, C. B.: Measuring Social Networks' Effects on
Agricultural Technology Adoption, Am. J. Agr.
Econ., 95, 353–359, https://doi.org/10.1093/ajae/aas049, 2013.
McConkey, B. G., Campbell, C. A., Zentner, R. P., Peru, M., and
VandenBygaart, A. J.: Effect of tillage and cropping frequency on
sustainable agriculture in the brown soil zone, Prairie Soils & Crops
Journal, 5, 51–58, 2012.
McDermid, S. S., Mearns, L. O., and Ruane, A. C.: Representing agriculture
in Earth System Models: Approaches and priorities for development, J.
Adv. Model. Earth Sy., 9, 2230–2265, https://doi.org/10.1002/2016MS000749, 2017.
Mitchell, J. P., Carter, L., Munk, D. S., Klonsky, K. M., Hutmacher, R. B.,
Shrestha, A., DeMoura, R., and Wroble, J. F.: Conservation tillage systems
for cotton advance in the San Joaquin Valley, Calif. Agr., 66,
108–115, https://doi.org/10.3733/ca.v066n03p108, 2012.
Montgomery, D. R.: Soil erosion and agricultural sustainability, P.
Natl. Acad. Sci. USA, 104, 13268–13272, https://doi.org/10.1073/pnas.0611508104, 2007.
Nachtergaele, F. O., Petri, M., Biancalani, R., van Lynden, G., and van
Velthuizen, H.: Global Land Degradation Information System (GLADIS), An
information database for land degradation assessment at global level,
Technical report of the LADA FAO/UNEP Project, available at:
http://www.fao.org/fileadmin/templates/solaw/files/thematic_reports/SOLAW_thematic_report_3_land_degradation.pdf (last access: 28 November 2019), 2011.
Ngwira, A. R., Aune, J. B., and Mkwinda, S.: On-farm evaluation of yield and
economic benefit of short term maize legume intercropping systems under
conservation agriculture in Malawi, Field Crop. Res., 132, 149–157, https://doi.org/10.1016/j.fcr.2011.12.014, 2012.
Nyakatawa, E. Z., Jakkula, V., Reddy, K. C., Lemunyon, J. L., and Norris, B.
E.: Soil erosion estimation in conservation tillage systems with poultry
litter application using RUSLE 2.0 model, Soil Till. Res., 94,
410–419, https://doi.org/10.1016/j.still.2006.09.003, 2007.
Nychka, D., Furrer, R., Paige, J., and Sain, S.: fields: Tools for Spatial
Data. R package version 8.3-6, available at: http://CRAN.R-project.org/package=fields (last access: 27 November 2018),
2016.
Olin, S., Lindeskog, M., Pugh, T. A. M., Schurgers, G., Wårlind, D., Mishurov, M., Zaehle, S.,
Stocker, B. D., Smith, B., and Arneth, A.: Soil carbon management in large-scale Earth system
modelling: implications for crop yields and nitrogen leaching, Earth Syst.
Dynam., 6, 745–768, https://doi.org/10.5194/esd-6-745-2015, 2015.
Pac, S. N.: Update! Evolution of No Till adoption in Argentina, Argentine No
till Farmers Association (Aapresid), 1–5, 2018.
Panagos, P., Borrelli, P., Meusburger, K., Alewell, C., Lugato, E., and
Montanarella, L.: Estimating the soil erosion cover-management factor at the
European scale, Land Use Policy, 48, 38–50, https://doi.org/10.1016/j.landusepol.2015.05.021, 2015.
Pierce, D.: Interface to Unidata netCDF (Version 4 or Earlier) Format Data
Files. R package version 1.15, available at: http://CRAN.R-project.org/package=ncdf4 (last access: 27 November 2018),
2015.
Pimental, D. and Sparks, D. L.: Soil as an endangered ecosystem, BioScience,
50, 947–947, https://doi.org/10.1641/0006-3568(2000)050[0947:SAAEE]2.0.CO;2, 2000.
Pittelkow, C. M., Linquist, B. A., Lundy, M. E., Liang, X., van Groenigen,
K. J., Lee, J., van Gestel, N., Six, J., Venterea, R. T., and van Kessel,
C.: When does no-till yield more? A global meta-analysis, Field Crop.
Res., 183, 156–168, https://doi.org/10.1016/j.fcr.2015.07.020, 2015.
Pongratz, J., Dolman, H., Don, A., Erb, K.-H., Fuchs, R., Herold, M., Jones,
C., Kuemmerle, T., Luyssaert, S., Meyfroidt, P., and Naudts, K.: Models meet
data: Challenges and opportunities in implementing land management in Earth
system models, Glob. Change Biol., 24, 1470–1487, https://doi.org/10.1111/gcb.13988,
2018.
Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F.,
Stehfest, E., Bodirsky, B. L., Dietrich, J. P., Doelmann, J. C., Gusti, M.,
Hasegawa, T., Kyle, P., Obersteiner, M., Tabeau, A., Takahashi, K., Valin,
H., Waldhoff, S., Weindl, I., Wise, M., Kriegler, E., Lotze-Campen, H.,
Fricko, O., Riahi, K., and Vuuren, D. P. v.: Land-use futures in the shared
socio-economic pathways, Global Environ. Chang., 42, 331–345, https://doi.org/10.1016/j.gloenvcha.2016.10.002, 2017.
Porwollik, V., Rolinski, S., and Müller, C.: A global gridded data set
on tillage – R-code (V. 1.1), GFZ Data Services, https://doi.org/10.5880/PIK.2019.010,
2019a.
Porwollik, V., Rolinski, S., and Müller, C.: A global gridded data set
on tillage (V. 1.1), GFZ Data Services, https://doi.org/10.5880/PIK.2019.009, 2019b.
Powlson, D. S., Stirling, C. M., Jat, M. L., Gerard, B. G., Palm, C. A.,
Sanchez, P. A., and Cassman, K. G.: Limited potential of no-till agriculture
for climate change mitigation, Nat. Clim. Change, 4, 678–683, https://doi.org/10.1038/nclimate2292, 2014.
Prestele, R., Hirsch, A. L., Davin, E. L., Seneviratne, S. I., and Verburg,
P. H.: A spatially explicit representation of conservation agriculture for
application in global change studies, Glob. Change Biol., 24, 4038–4053,
https://doi.org/10.1111/gcb.14307, 2018.
Pugh, T. A. M., Arneth, A., Olin, S., Ahlström, A., Bayer, A. D., Klein
Goldewijk, K., Lindeskog, M., and Schurgers, G.: Simulated carbon emissions
from land-use change are substantially enhanced by accounting for
agricultural management, Environ. Res. Lett., 10, 124008, https://doi.org/10.1088/1748-9326/10/12/124008, 2015.
Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A.: Farming the
planet: 1. Geographic distribution of global agricultural lands in the year
2000, Global Biogeochem. Cy., 22, GB1003, https://doi.org/10.1029/2007GB002952,
2008.
R Development Core Team: R: A language and
environment for statistical computing, R Foundation for Statistical
Computing, Vienna, Austria, ISBN 3-900051-07-0, available at:
http://www.R-project.org (last access: 17 July 2018), 2013.
Rosegrant, M. W., Koo, J., Cenacchi, N., Ringler, C., Robertson, R. D.,
Fisher, M., Cox, C. M., Garrett, K., Perez, N. D., and Sabbagh, P.: Food
security in a world of natural resource scarcity: The role of agricultural
technologies, International Food Policy Research Institute (IFPRI),
Washington, DC, 2014.
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C.,
Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann,
K., Piontek, F., Pugh, T. A. M., Schmid, E., Stehfest, E., Yang, H., and
Jones, J. W.: Assessing agricultural risks of climate change in the 21st
century in a global gridded crop model intercomparison, P.
Natl. Acad. Sci. USA, 111, 3268–3273, https://doi.org/10.1073/pnas.1222463110,
2014.
Sá, J. C. M., Burkner dos Santos, J., and Lal, R.: An on-farm assessment
of carbon monitoring and mapping scaling up in no-till fields, Food and
Agriculture Organization of the United Nations (FAO), State University of
Ponta Grossa, 2012.
Saharawat, Y. S., Singh, B., Malik, R. K., Ladha, J. K., Gathala, M., Jat,
M. L., and Kumar, V.: Evaluation of alternative tillage and crop
establishment methods in a rice–wheat rotation in North Western IGP, Field
Crop. Res., 116, 260–267, https://doi.org/10.1016/j.fcr.2010.01.003, 2010.
Schmitz, M., Puran, M., and Hesse, J. W.: The Importance of Conservation
Tillage as a Contribution to Sustainable Agriculture: A special Case of Soil
Erosion, Institut für Agribusiness, Gießen, Germany, 2015.
Scopel, E., Triomphe, B., Affholder, F., Da Silva, F. A. M., Corbeels, M.,
Xavier, J. H. V., Lahmar, R., Recous, S., Bernoux, M., Blanchart, E., de
Carvalho Mendes, I., and De Tourdonnet, S.: Conservation agriculture
cropping systems in temperate and tropical conditions, performances and
impacts. A review, Agron. Sustain. Dev., 33, 113–130, https://doi.org/10.1007/s13593-012-0106-9, 2013.
Siebert, S., Portmann, F. T., and Döll, P.: Global Patterns of Cropland
Use Intensity, Remote Sensing, 2, 1625–1643, https://doi.org/10.3390/rs2071625, 2010.
Siebert, S., Kummu, M., Porkka, M., Döll, P., Ramankutty, N., and Scanlon, B. R.: A global data set of the extent of
irrigated land from 1900 to 2005, Hydrol. Earth Syst. Sci., 19, 1521–1545, https://doi.org/10.5194/hess-19-1521-2015, 2015.
Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kumar, P., McCarl,
B., Ogle, S., O'Mara, F., Rice, C., Scholes, B., Sirotenko, O., Howden, M.,
McAllister, T., Pan, G., Romanenkov, V., Schneider, U., Towprayoon, S.,
Wattenbach, M., and Smith, J.: Greenhouse gas mitigation in agriculture,
Philo. T. Roy. Soc. B, 363,
789–813, https://doi.org/10.1098/rstb.2007.2184, 2008.
van Asselen, S. and Verburg, P. H.: A land system representation for global
assessments and land-use modeling, Glob. Change Biol., 18, 3125–3148,
https://doi.org/10.1111/j.1365-2486.2012.02759.x, 2012.
van de Steeg, J. A.: Characterization of the spatial distribution of farming
systems in the Kenyan Highlands, Appl. Geogr., 30, 239–253,
https://doi.org/10.1016/j.apgeog.2009.05.005, 2010.
Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., and
Mastura, S. S. A.: Modeling the Spatial Dynamics of Regional Land Use: The
CLUE-S Model, Environ. Manage., 30, 391–405, https://doi.org/10.1007/s00267-002-2630-x, 2002.
Waha, K., van Bussel, L. G. J., 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.
Ward, P. S., Bell, A. R., Droppelmann, K., and Benton, T. G.: Early adoption
of conservation agriculture practices: Understanding partial compliance in
programs with multiple adoption decisions, Land Use Policy, 70, 27–37, https://doi.org/10.1016/j.landusepol.2017.10.001, 2018.
White, J. W., Jones, J. W., Porter, C., McMaster, G. S., and Sommer, R.:
Issues of spatial and temporal scale in modeling the effects of field
operations on soil properties, Oper. Res., 10, 279–299, https://doi.org/10.1007/s12351-009-0067-1, 2010.
Wischmeier, W. H. and Smith, D. D.: Predicting rainfall erosion losses. A
guide to conservation planning, Washington, DC, 1978.
World Bank: World Development indicators- historical classification by
income, available at:
https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (last access: 29 November 2018),
2017.
You, L., Wood, S., Wood-Sichra, U., and Wu, W.: Generating global crop
distribution maps: From census to grid, Agr. Syst., 127, 53–60,
https://doi.org/10.1016/j.agsy.2014.01.002, 2014.
Young, D. L. and Schillinger, W. F.: Wheat farmers adopt the undercutter
fallow method to reduce wind erosion and sustain profitability, Soil
Till. Res., 124, 240–244, https://doi.org/10.1016/j.still.2012.07.001, 2012.
Short summary
This study describes the generation of a classification and the global spatially explicit mapping of six crop-specific tillage systems for around the year 2005. Tillage practices differ by the kind of equipment used, soil surface and depth affected, timing, and their purpose within the cropping systems. The identified tillage systems including a downscale algorithm of national Conservation Agriculture area values were allocated to crop-specific cropland areas with a resolution of 5 arcmin.
This study describes the generation of a classification and the global spatially explicit...
Altmetrics
Final-revised paper
Preprint