Articles | Volume 18, issue 9
https://doi.org/10.5194/essd-18-6251-2026
© Author(s) 2026. 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-18-6251-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
01 Sep 2026
Data description article |
| 01 Sep 2026
| 01 Sep 2026
Spatial patterns of global land management intensity are influenced by socioeconomic, biophysical and behavioural factors
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467, Garmisch-Partenkirchen, Germany
Calum Brown
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467, Garmisch-Partenkirchen, Germany
Karina Winkler
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467, Garmisch-Partenkirchen, Germany
Almut Arneth
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467, Garmisch-Partenkirchen, Germany
Institute of Geography and Geo-ecology, Karlsruhe Institute of Technology, Kaiserstraße 12, Building 10.50, 76131, Karlsruhe, Germany
Mark Rounsevell
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467, Garmisch-Partenkirchen, Germany
Institute of Geography and Geo-ecology, Karlsruhe Institute of Technology, Kaiserstraße 12, Building 10.50, 76131, Karlsruhe, Germany
School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh, EH8 9XP, UK
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Katharina Ramm, Calum Brown, Almut Arneth, and Mark Rounsevell
EGUsphere, https://doi.org/10.64898/2026.04.16.718867, https://doi.org/10.64898/2026.04.16.718867, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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Human activities are having negative impacts on biodiversity worldwide. We have developed the Biodiversity Pressure Index to track the five key threats — land use change, resource extraction, climate change, pollution, and invasive species — across the entire planet from 1990 to 2020. Results show nearly 90 % of Earth's land is under medium or high human pressure, and 96 % has worsened over 30 years. Among other pressures, rising temperatures and global trade drive much of this damage.
Jens Krause, Peter Anthoni, Mike Harfoot, Moritz Kupisch, and Almut Arneth
Geosci. Model Dev., 18, 9633–9651, https://doi.org/10.5194/gmd-18-9633-2025, https://doi.org/10.5194/gmd-18-9633-2025, 2025
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While animal biodiversity is facing a global crisis as more and more species are becoming endangered or extinct, the role of animals for the functioning of ecosystems is still not fully understood. We contribute to bridging this gap by coupling a animal population model with a vegetation and thus enable future research in this topic.
Dmitry Otryakhin, David Martín Belda, and Almut Arneth
Geosci. Model Dev., 18, 9101–9118, https://doi.org/10.5194/gmd-18-9101-2025, https://doi.org/10.5194/gmd-18-9101-2025, 2025
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We developed a methodology for comparison of simulation results by a dynamic global vegetation model (DGVM). Using this methodology, we reveal systematic differences between high- and low-resolution DGVM simulations caused by under-representation of climate variability in the low-resolution data and poor representation of shore lines and inland water bodies. In a study area covering European Union, the differences in aggregated output variables were found to be 2.8%–7.3%.
Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell
Geosci. Model Dev., 18, 4983–5013, https://doi.org/10.5194/gmd-18-4983-2025, https://doi.org/10.5194/gmd-18-4983-2025, 2025
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Understanding environmental policy interventions is challenging due to complex institutional actor interactions. Large language models (LLMs) offer new solutions by mimicking the actors. We present InsNet-CRAFTY v1.0, a multi-LLM-agent model coupled with a land system model, simulating competing policy priorities. The model shows how LLM agents can simulate decision-making in institutional networks, highlighting both their potential and limitations in advancing land system modelling.
Rubaya Pervin, Scott Robeson, Mallory Barnes, Stephen Sitch, Anthony Walker, Ben Poulter, Fabienne Maignan, Qing Sun, Thomas Colligan, Sönke Zaehle, Kashif Mahmud, Peter Anthoni, Almut Arneth, Vivek Arora, Vladislav Bastrikov, Liam Bogucki, Bertrand Decharme, Christine Delire, Stefanie Falk, Akihiko Ito, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Michael O’Sullivan, Wenping Yuan, and Natasha MacBean
EGUsphere, https://doi.org/10.5194/egusphere-2025-2841, https://doi.org/10.5194/egusphere-2025-2841, 2025
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Drylands contribute more than a third of the global vegetation productivity. Yet, these regions are not well represented in global vegetation models. Here, we tested how well 15 global models capture annual changes in dryland vegetation productivity. Models that didn’t have vegetation change over time or fire have lower variability in vegetation productivity. Models need better representation of grass cover types and their coverage. Our work highlights where and how these models need to improve.
Carolina Natel, David Martín Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
Geosci. Model Dev., 18, 4317–4333, https://doi.org/10.5194/gmd-18-4317-2025, https://doi.org/10.5194/gmd-18-4317-2025, 2025
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We developed fast machine learning models to predict forest regrowth and carbon dynamics under climate change. These models mimic the outputs of a complex vegetation model but run 95 % faster, enabling global analyses and supporting climate solutions in large modeling frameworks such as LandSyMM.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
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Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
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
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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.
Yongchao Zeng, Calum Brown, Joanna Raymond, Mohamed Byari, Ronja Hotz, and Mark Rounsevell
Earth Syst. Dynam., 16, 423–449, https://doi.org/10.5194/esd-16-423-2025, https://doi.org/10.5194/esd-16-423-2025, 2025
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This study explores using large language models (LLMs) to simulate policy-making in land systems. We integrated LLMs into a land use model and simulated LLM-powered institutional agents steering meat production by taxation. The results show LLMs can generate boundedly rational policy-making behaviours that can hardly be modelled using conventional methods; LLMs can offer the reasoning behind policy actions. We also discussed LLMs’ potential and challenges in large-scale simulations.
Matthew J. McGrath, Ana Maria Roxana Petrescu, Philippe Peylin, Robbie M. Andrew, Bradley Matthews, Frank Dentener, Juraj Balkovič, Vladislav Bastrikov, Meike Becker, Gregoire Broquet, Philippe Ciais, Audrey Fortems-Cheiney, Raphael Ganzenmüller, Giacomo Grassi, Ian Harris, Matthew Jones, Jürgen Knauer, Matthias Kuhnert, Guillaume Monteil, Saqr Munassar, Paul I. Palmer, Glen P. Peters, Chunjing Qiu, Mart-Jan Schelhaas, Oksana Tarasova, Matteo Vizzarri, Karina Winkler, Gianpaolo Balsamo, Antoine Berchet, Peter Briggs, Patrick Brockmann, Frédéric Chevallier, Giulia Conchedda, Monica Crippa, Stijn N. C. Dellaert, Hugo A. C. Denier van der Gon, Sara Filipek, Pierre Friedlingstein, Richard Fuchs, Michael Gauss, Christoph Gerbig, Diego Guizzardi, Dirk Günther, Richard A. Houghton, Greet Janssens-Maenhout, Ronny Lauerwald, Bas Lerink, Ingrid T. Luijkx, Géraud Moulas, Marilena Muntean, Gert-Jan Nabuurs, Aurélie Paquirissamy, Lucia Perugini, Wouter Peters, Roberto Pilli, Julia Pongratz, Pierre Regnier, Marko Scholze, Yusuf Serengil, Pete Smith, Efisio Solazzo, Rona L. Thompson, Francesco N. Tubiello, Timo Vesala, and Sophia Walther
Earth Syst. Sci. Data, 15, 4295–4370, https://doi.org/10.5194/essd-15-4295-2023, https://doi.org/10.5194/essd-15-4295-2023, 2023
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Accurate estimation of fluxes of carbon dioxide from the land surface is essential for understanding future impacts of greenhouse gas emissions on the climate system. A wide variety of methods currently exist to estimate these sources and sinks. We are continuing work to develop annual comparisons of these diverse methods in order to clarify what they all actually calculate and to resolve apparent disagreement, in addition to highlighting opportunities for increased understanding.
Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul K. Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle
Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023, https://doi.org/10.5194/esd-14-767-2023, 2023
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Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that variability in N processes across models is large. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain, and more are necessary to guide the development of N cycling in models.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, 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, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
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The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. 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.
David Martín Belda, Peter Anthoni, David Wårlind, Stefan Olin, Guy Schurgers, Jing Tang, Benjamin Smith, and Almut Arneth
Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, https://doi.org/10.5194/gmd-15-6709-2022, 2022
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We present a number of augmentations to the ecosystem model LPJ-GUESS, which will allow us to use it in studies of the interactions between the land biosphere and the climate. The new module enables calculation of fluxes of energy and water into the atmosphere that are consistent with the modelled vegetation processes. The modelled fluxes are in fair agreement with observations across 21 sites from the FLUXNET network.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev., 15, 6495–6519, https://doi.org/10.5194/gmd-15-6495-2022, https://doi.org/10.5194/gmd-15-6495-2022, 2022
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Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
Jianyong Ma, Sam S. Rabin, Peter Anthoni, Anita D. Bayer, Sylvia S. Nyawira, Stefan Olin, Longlong Xia, and Almut Arneth
Biogeosciences, 19, 2145–2169, https://doi.org/10.5194/bg-19-2145-2022, https://doi.org/10.5194/bg-19-2145-2022, 2022
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Improved agricultural management plays a vital role in protecting soils from degradation in eastern Africa. We simulated the impacts of seven management practices on soil carbon pools, nitrogen loss, and crop yield under different climate scenarios in this region. This study highlights the possibilities of conservation agriculture when targeting long-term environmental sustainability and food security in crop ecosystems, particularly for those with poor soil conditions in tropical climates.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Jianyong Ma, Stefan Olin, Peter Anthoni, Sam S. Rabin, Anita D. Bayer, Sylvia S. Nyawira, and Almut Arneth
Geosci. Model Dev., 15, 815–839, https://doi.org/10.5194/gmd-15-815-2022, https://doi.org/10.5194/gmd-15-815-2022, 2022
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The implementation of the biological N fixation process in LPJ-GUESS in this study provides an opportunity to quantify N fixation rates between legumes and to better estimate grain legume production on a global scale. It also helps to predict and detect the potential contribution of N-fixing plants as
green manureto reducing or removing the use of N fertilizer in global agricultural systems, considering different climate conditions, management practices, and land-use change scenarios.
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, https://doi.org/10.5194/esd-12-1015-2021, 2021
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Temperate biomes in Europe are not prone to recurrent dry and hot conditions in summer. However, these conditions may become more frequent in the coming decades. Because stress conditions can leave legacies for many years, this may result in reduced ecosystem resilience under recurrent stress. We assess vegetation vulnerability to the hot and dry summers in 2018 and 2019 in Europe and find the important role of inter-annual legacy effects from 2018 in modulating the impacts of the 2019 event.
Alexander J. Winkler, Ranga B. Myneni, Alexis Hannart, Stephen Sitch, Vanessa Haverd, Danica Lombardozzi, Vivek K. Arora, Julia Pongratz, Julia E. M. S. Nabel, Daniel S. Goll, Etsushi Kato, Hanqin Tian, Almut Arneth, Pierre Friedlingstein, Atul K. Jain, Sönke Zaehle, and Victor Brovkin
Biogeosciences, 18, 4985–5010, https://doi.org/10.5194/bg-18-4985-2021, https://doi.org/10.5194/bg-18-4985-2021, 2021
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Satellite observations since the early 1980s show that Earth's greening trend is slowing down and that browning clusters have been emerging, especially in the last 2 decades. A collection of model simulations in conjunction with causal theory points at climatic changes as a key driver of vegetation changes in natural ecosystems. Most models underestimate the observed vegetation browning, especially in tropical rainforests, which could be due to an excessive CO2 fertilization effect in models.
Cited articles
Adalibieke, W., Cui, X., Cai, H., You, L., and Zhou, F.: Global crop-specific nitrogen fertilization dataset in 1961–2020, Sci. Data, 10, 617, https://doi.org/10.1038/s41597-023-02526-z, 2023.
Alexander, P., Rounsevell, M. D., Dislich, C., Dodson, J. R., Engström, K., and Moran, D.: Drivers for global agricultural land use change: The nexus of diet, population, yield and bioenergy, Global Environ. Change, 35, 138–147, 2015.
Arneth, A., Alexander, P., Fuchs, R., Shin, Y., Claudet, J., Coll, M., Leadley, P., Rondinini, C., and Rounsevell, M. D. A.: The biodiversity-climate-food nexus: illustrating challenges and solutions using the Green Shoots framework, One Earth, https://doi.org/10.1016/j.oneear.2025.101455, 2025.
Bahar, N. H., Lo, M., Sanjaya, M., Van Vianen, J., Alexander, P., Ickowitz, A., and Sunderland, T.: Meeting the food security challenge for nine billion people in 2050: What impact on forests?, Global Environ. Change, 62, 102056, https://doi.org/10.1016/j.gloenvcha.2020.102056, 2020.
Bayer, A. D., Lautenbach, S., and Arneth, A.: Benefits and trade-offs of optimizing global land use for food, water, and carbon, Proc. Natl. Acad. Sci. USA, 120, e2220371120, https://doi.org/10.1073/pnas.2220371120, 2023.
Brown, C., Alexander, P., Holzhauer, S., and Rounsevell, M. D.: Behavioral models of climate change adaptation and mitigation in land-based sectors, WIREs Clim. Change, 8, e448, https://doi.org/10.1002/wcc.448, 2017.
Dou, Y., Cosentino, F., Malek, Z., Maiorano, L., Thuiller, W., and Verburg, P. H.: A new European land systems representation accounting for landscape characteristics, Landscape Ecol., 36, 2215–2234, 2021.
Earth Engine Data Catalog: Global Pasture Watch, https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch (last access: 27 August 2026), 2026.
EarthStat: EarthStat, http://www.earthstat.org, last access: 19 August 2026.
Erb, K. H., Lauk, C., Kastner, T., Mayer, A., Theurl, M. C., and Haberl, H.: Exploring the biophysical option space for feeding the world without deforestation, Nat. Commun., 7, 11382, https://doi.org/10.1038/ncomms11382, 2016.
Erb, K. H., Luyssaert, S., Meyfroidt, P., Pongratz, J., Don, A., Kloster, S., Kuemmerle, T., Fetzel, T., Fuchs, R., Herold, M., and Haberl, H.: Land management: data availability and process understanding for global change studies, Glob. Change Biol., 23, 512–533, 2017.
FAO: Global Forest Resources Assessment 2020: Main report, Food and Agriculture Organization of the United Nations, https://www.fao.org/forest-resources-assessment/en/ (last access: 27 August 2026), 2020.
FAO: FAOSTAT Statistical Database, Food and Agriculture Organization of the United Nations, http://www.fao.org/faostat (last access: 27 August 2026), 2022.
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O'Connell, C., Ray, D. K., West, P. C., and Balzer, C.: Solutions for a cultivated planet, Nature, 478, 337–342, 2011.
Food and Agriculture Organization of the United Nations (FAO): Agricultural production data, Our World in Data, https://ourworldindata.org/agricultural-production (last access: 27 August 2026), 2024a.
Food and Agriculture Organization of the United Nations (FAO): Forest area, Our World in Data, https://ourworldindata.org/forest-area (last access: 27 August 2026), 2024b.
Fraunhofer ISE: Photovoltaics Report, Fraunhofer Institute for Solar Energy Systems ISE, https://www.ise.fraunhofer.de/en/publications/studies/photovoltaics-report.html (last access: 27 August 2026), 2023.
Global Forest Review: Forest Extent Indicator, World Resources Institute, Washington, DC, https://research.wri.org/gfr/forest-extent-indicators/forest-extent (last access: 27 August 2026), 2024.
Haberl, H., Erb, K. H., and Krausmann, F.: Human appropriation of net primary production: patterns, trends, and planetary boundaries, Annu. Rev. Environ. Resour., 39, 363–391, 2014.
Harrison, G. R., Rigge, M., Assal, T. J., Applestein, C., James, D. K., and McCord, S. E.: An accuracy assessment of satellite-derived rangeland fractional cover, Ecol. Indic., 172, 113267, https://doi.org/10.1016/j.ecolind.2025.113267, 2025.
Herrero, M., Havlík, P., Valin, H., Notenbaert, A., Rufino, M. C., Thornton, P. K., Blümmel, M., Weiss, F., Grace, D., and Obersteiner, M.: Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems, Proc. Natl. Acad. Sci. USA, 110, 20888–20893, 2013.
IPCC: Special Report on Climate Change and Land (SRCCL), Intergovernmental Panel on Climate Change, https://www.ipcc.ch/srccl/ (last access: 27 August 2026), 2022a.
IPCC: Sixth Assessment Report: Working Group III – Climate Change Mitigation, Intergovernmental Panel on Climate Change, https://www.ipcc.ch/report/ar6/wg3/ (last access: 27 August 2026), 2022b.
IRENA: Renewable Capacity Statistics 2023, International Renewable Energy Agency, https://www.irena.org/Statistics (last access: 27 August 2026), 2023.
Jayne, T. S., Snapp, S., Place, F., and Sitko, N.: Sustainable agricultural intensification in an era of rural transformation in Africa, Global Food Secur., 20, 105–113, 2019.
Kastner, T., Matej, S., Forrest, M., Gingrich, S., Haberl, H., Hickler, T., Krausmann, F., Lasslop, G., Niedertscheider, M., Plutzar, C., and Schwarzmüller, F.: Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century, Glob. Change Biol., 28, 307–322, 2022.
Kay, M., Bunning, S., Burke, J., Boerger, V., Bojic, D., Bosc, P. M., Clark, M., Dale, D., England, M., Hoogeveen, J., and Koo-Oshima, S.: The state of the world's land and water resources for food and agriculture 2021: Systems at breaking point, https://openknowledge.fao.org/handle/20.500.14283/i1688e (last access: 27 August 2026), 2022.
Kebede, E. A., Oluoch, K. O. A., Siebert, S., Mehta, P., Hartman, S., Jägermeyr, J., Ray, D., Ali, T., Brauman, K. A., Deng, Q., and Xie, W.: A global open-source dataset of monthly irrigated and rainfed cropped areas (MIRCA-OS) for the 21st century, Sci. Data, 12, 208, https://doi.org/10.1038/s41597-024-04313-w, 2025.
Kehoe, L., Kuemmerle, T., Meyer, C., Levers, C., Václavík, T., and Kreft, H.: Global patterns of agricultural land-use intensity and vertebrate diversity, Divers. Distrib., 21, 1308–1318, 2015.
Kleijn, D., Kohler, F., Báldi, A., Batáry, P., Concepción, E. D., Clough, Y., Díaz, M., Gabriel, D., Holzschuh, A., Knop, E., and Kovács, A.: On the relationship between farmland biodiversity and land-use intensity in Europe, Proc. R. Soc. B, 276, 903–909, 2009.
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.
Lambin, E. F. and Meyfroidt, P.: Global land use change, economic globalization, and the looming land scarcity, Proc. Natl. Acad. Sci. USA, 108, 3465–3472, 2011.
Lesiv, M., Schepaschenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., and Blyshchyk, V.: Global forest management data for 2015 at a 100 m resolution, Sci. Data, 9, 199, https://doi.org/10.1038/s41597-022-01332-3, 2022.
Li, A., Liu, L., Li, S., Cui, X., Chen, X., and Cao, X.: Global photovoltaic solar panel dataset from 2019 to 2022, Sci. Data, 12, 637, https://doi.org/10.1038/s41597-025-04985-y, 2025.
Liu, X., Zheng, J., Yu, L., Hao, P., Chen, B., Xin, Q., Fu, H., and Gong, P.: Annual dynamic dataset of global cropping intensity from 2001 to 2019, Sci. Data, 8, 283, https://doi.org/10.1038/s41597-021-01065-9, 2021.
Ma, H., Crowther, T. W., Mo, L., Maynard, D. S., Renner, S. S., Van den Hoogen, J., Zou, Y., Liang, J., De-Miguel, S., Nabuurs, G. J., and Reich, P. B.: The global biogeography of tree leaf form and habit, Nat. Plants, 9, 1795–1809, 2023.
Ma, J., Tian, L., Zhang, Y., Yang, X., Li, Y., Liu, Z., Zhou, L., Wang, Z., and Ouyang, W.: Global property rights and land use efficiency, Nat. Commun., 15, 8525, https://doi.org/10.1038/s41467-024-52859-5, 2024.
Malek, Z̆., Douw, B., Van Vliet, J., Van Der Zanden, E. H., and Verburg, P. H.: Local land-use decision-making in a global context, Environ. Res. Lett., 14, 083006, https://doi.org/10.1088/1748-9326/ab309e, 2019.
Malek, Z̆. and Verburg, P. H.: Mapping global patterns of land use decision-making, Global Environ. Change, 65, 102170, https://doi.org/10.1016/j.gloenvcha.2020.102170, 2020.
Matej, S., Weidinger, F., Kaufmann, L., Roux, N., Gingrich, S., Haberl, H., Krausmann, F., and Erb, K. H.: A global land-use data cube 1992–2020 based on the Human Appropriation of Net Primary Production, Sci. Data, 12, 511, https://doi.org/10.1038/s41597-025-04788-1, 2025.
Mehta, P., Siebert, S., Kummu, M., Deng, Q., Ali, T., Marston, L., Xie, W., and Davis, K. F.: Half of twenty-first century global irrigation expansion has been in water-stressed regions, Nat. Water, 2, 254–261, 2024.
Meyfroidt, P., De Bremond, A., Ryan, C. M., Archer, E., Aspinall, R., Chhabra, A., Camara, G., Corbera, E., DeFries, R., Díaz, S., and Dong, J.: Ten facts about land systems for sustainability, Proc. Natl. Acad. Sci. USA, 119, e2109217118, https://doi.org/10.1016/j.gloenvcha.2018.08.006, 2022.
Nagaraj, D., Proust, E., Todeschini, A., Rulli, M. C., and D'Odorico, P.: A new dataset of global irrigation areas from 2001 to 2015, Adv. Water Resour., 152, 103910, https://doi.org/10.1016/j.advwatres.2021.103910, 2021.
NASA/METI/AIST/Japan Spacesystems and US/Japan ASTER Science Team: ASTER Global Digital Elevation Model V003, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/ASTER/ASTGTM.003, 2018.
Ostrom, E.: A general framework for analyzing sustainability of social-ecological systems, Science, 325, 419–422, 2009.
Overmars, K. P., Schulp, C. J., Alkemade, R., Verburg, P. H., Temme, A. J., Omtzigt, N., and Schaminée, J. H.: Developing a methodology for a species-based and spatially explicit indicator for biodiversity on agricultural land in the EU, Ecol. Indic., 37, 186–198, 2014.
Pergner, I. and Lippert, C.: On the effects that motivate pesticide use in perspective of designing a cropping system without pesticides but with mineral fertilizer – a review, Agron. Sustain. Dev., 43, 24, https://doi.org/10.1007/s13593-023-00877-w, 2023.
Perkins, O., Saxena, A., Seo, B., Millington, J., Brown, C., and Rounsevell, M.: Gridded socio-economic capitals for the SSPs, Zenodo [data set], https://doi.org/10.5281/zenodo.17580780, 2025.
Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F., Stehfest, E., Bodirsky, B. L., Dietrich, J. P., Doelmann, J. C., Gusti, M., and Hasegawa, T.: Land-use futures in the shared socio-economic pathways, Global Environ. Change, 42, 331–345, 2017.
Rounsevell, M. D. and Arneth, A.: Representing human behaviour and decisional processes in land system models as an integral component of the earth system, Global Environ. Change, 21, 840–843, 2011.
Rounsevell, M. D., Arneth, A., Brown, C., Cheung, W. W., Gimenez, O., Holman, I., Leadley, P., Luján, C., Mahevas, S., Maréchaux, I., and Pélissier, R.: Identifying uncertainties in scenarios and models of socio-ecological systems in support of decision-making, One Earth, 4, 967–985, 2021.
Rudel, T. K., Schneider, L., and Uriarte, M.: Forest transitions: An introduction, Land Use Policy, 27, 95–97, 2010.
Saxena, A., Brown, C., Winkler, K., Arneth, A., and Rounsevell, M.: Global Land Management data explorer, https://ee-ankitasaxena03as.projects.earthengine.app/view/global-land-use-management (last access: 27 August 2026), 2025.
Saxena, A., Brown, C., Winkler, K., Arneth, A., and Rounsevell, M.: Global Gridded Land Management Dataset at 0.01-degrees, Zenodo [data set], https://doi.org/10.5281/zenodo.18249970, 2026.
Stehfest, E., van Zeist, W. J., Valin, H., Havlik, P., Popp, A., Kyle, P., Tabeau, A., Mason-D'Croz, D., Hasegawa, T., Bodirsky, B. L., and Calvin, K.: Key determinants of global land-use projections, Nat. Commun., 10, 2166, https://doi.org/10.1038/s41467-019-09945-w, 2019.
Tang, F. H., Nguyen, T. H., Conchedda, G., Casse, L., Tubiello, F. N., and Maggi, F.: CROPGRIDS: a global geo-referenced dataset of 173 crops, Sci. Data, 11, 413, https://doi.org/10.1038/s41597-024-03247-7, 2024.
Tian, H., Bian, Z., Shi, H., Qin, X., Pan, N., Lu, C., Pan, S., Tubiello, F. N., Chang, J., Conchedda, G., Liu, J., Mueller, N., Nishina, K., Xu, R., Yang, J., You, L., and Zhang, B.: History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere: a 5 arcmin resolution annual dataset from 1860 to 2019, Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, 2022.
Turner, B., Devisscher, T., Chabaneix, N., Woroniecki, S., Messier, C., and Seddon, N.: The role of nature-based solutions in supporting social-ecological resilience for climate change adaptation, Annu. Rev. Environ. Resour., 47, 123–148, 2022.
UN DESA: World Urbanization Prospects: The 2018 Revision, United Nations Department of Economic and Social Affairs, Population Division, https://population.un.org/wup/ (last access: 27 August 2026), 2018.
Vortkamp, I. and Hilker, F. M.: Farmers' land-use decision-making: A dynamical modelling approach that integrates qualitative knowledge about social norms into a quantitative model, People Nat., 5, 1147–1159, 2023.
Winkler, K., Fuchs, R., Rounsevell, M., and Herold, M.: HILDA+ Global land use change between 1960 and 2019, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.921846, 2020.
Winkler, K., Fuchs, R., Rounsevell, M., and Herold, M.: Global land use changes are four times greater than previously estimated, Nat. Commun., 12, 2501, https://doi.org/10.1038/s41467-021-22702-2, 2021.
Winkler, K., Fuchs, R., Rounsevell, M., and Herold, M.: Six decades of global crop yield increase and cropland expansion from 1960 to 2020, Environ. Res. Commun., 7, 055013, https://doi.org/10.1088/2515-7620/add3cd, 2025.
Zabel, F., Delzeit, R., Schneider, J. M., Seppelt, R., Mauser, W., and Václavík, T.: Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity, Nat. Commun., 10, 2844, https://doi.org/10.1038/s41467-019-10775-z, 2019.
Zomer, R. J., Xu, J., and Trabucco, A.: Version 3 of the global aridity index and potential evapotranspiration database, Sci. Data, 9, 409, https://doi.org/10.1038/s41597-022-01493-1, 2022.
Short summary
We present a globally harmonised land management intensity dataset at 0.01 ° resolution for 2020 and assess its relationship with socioeconomic, biophysical and behavioural factors. We find that economic growth doesn’t uniformly align with the intensive management instead human behaviour also influence how land is managed. Intensive management is highly concentrated with only a few countries accounting for a disproportionate share of global intensive cropland, pasture and forest management.
We present a globally harmonised land management intensity dataset at 0.01 ° resolution for 2020...
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