Articles | Volume 19, issue 12
https://doi.org/10.5194/bg-19-3021-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-3021-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
22 Jun 2022
Research article | Highlight paper |
| 22 Jun 2022
| 22 Jun 2022
Effects of climate change in European croplands and grasslands: productivity, greenhouse gas balance and soil carbon storage
Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 78850
Thiverval-Grignon, France
now at: Université Paris-Saclay, INRAE, AgroParisTech, UMR
SAD-APT, 78850 Thiverval-Grignon, France
Raphaël Martin
INRAE, UREP Unité de Recherche sur l'Ecosystème Prairial,
63100 Clermont-Ferrand, France
Katja Klumpp
INRAE, UREP Unité de Recherche sur l'Ecosystème Prairial,
63100 Clermont-Ferrand, France
Raia Silvia Massad
Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 78850
Thiverval-Grignon, France
Related authors
Victor Moinard, Antoine Savoie, Catherine Pasquier, Adeline Besnault, Yolaine Goubard-Delaunay, Baptiste Esnault, Marco Carozzi, Polina Voylokov, Sophie Génermont, Benjamin Loubet, Catherine Hénault, Florent Levavasseur, Jean-Marie Paillat, and Sabine Houot
EGUsphere, https://doi.org/10.5194/egusphere-2024-161, https://doi.org/10.5194/egusphere-2024-161, 2024
Preprint archived
Short summary
Short summary
Anaerobic digestion is used for biogas production. The resulting digestates may be associated with different crop performances and N losses compared to undigested animal effluents. We monitored N flows during a three-year field experiment with different fertilizations based on cattle effluents, digestates, or mineral fertilizers. Digestates were effective N fertilizer but required attention to NH3 volatilization. We identified no additional risks of N2O emissions with digestates.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
Short summary
Short summary
Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Benjamin Loubet, Marco Carozzi, Polina Voylokov, Jean-Pierre Cohan, Robert Trochard, and Sophie Génermont
Biogeosciences, 15, 3439–3460, https://doi.org/10.5194/bg-15-3439-2018, https://doi.org/10.5194/bg-15-3439-2018, 2018
Short summary
Short summary
Tropospheric ammonia is mainly emitted by agriculture. It constitutes a loss for the farmers and a threat to human health and the environment. It is therefore crucial to improve agricultural practices to reduce ammonia losses following fertilisation. In this study we propose an inverse dispersion modelling method to simultaneously quantify ammonia volatilisation from multiple small agronomic plots. The method was evaluated to be suitable (though slightly biased) based on a theoretical study.
Yang Liu, Raluca Ciuraru, Letizia Abis, Crist Amelynck, Pauline Buysse, Alex Guenther, Bernard Heinesch, Florence Lafouge, Florent Levavasseur, Benjamin Loubet, Auriane Voyard, and Raia-Silvia Massad
EGUsphere, https://doi.org/10.5194/egusphere-2024-530, https://doi.org/10.5194/egusphere-2024-530, 2024
Short summary
Short summary
This paper reviews the emission and emission processes of biogenic volatile organic compounds (BVOCs) from various crops and soil under different management practices, highlighting challenges in modeling the emissions and proposing a conceptual model for estimation. The aim of this paper is to present agricultural BVOC data and related mechanistic processes to enhance model accuracy and reduce uncertainties in estimating BVOC emissions from agriculture.
Victor Moinard, Antoine Savoie, Catherine Pasquier, Adeline Besnault, Yolaine Goubard-Delaunay, Baptiste Esnault, Marco Carozzi, Polina Voylokov, Sophie Génermont, Benjamin Loubet, Catherine Hénault, Florent Levavasseur, Jean-Marie Paillat, and Sabine Houot
EGUsphere, https://doi.org/10.5194/egusphere-2024-161, https://doi.org/10.5194/egusphere-2024-161, 2024
Preprint archived
Short summary
Short summary
Anaerobic digestion is used for biogas production. The resulting digestates may be associated with different crop performances and N losses compared to undigested animal effluents. We monitored N flows during a three-year field experiment with different fertilizations based on cattle effluents, digestates, or mineral fertilizers. Digestates were effective N fertilizer but required attention to NH3 volatilization. We identified no additional risks of N2O emissions with digestates.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
Short summary
Short summary
Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Raia Silvia Massad, Juliette Lathière, Susanna Strada, Mathieu Perrin, Erwan Personne, Marc Stéfanon, Patrick Stella, Sophie Szopa, and Nathalie de Noblet-Ducoudré
Biogeosciences, 16, 2369–2408, https://doi.org/10.5194/bg-16-2369-2019, https://doi.org/10.5194/bg-16-2369-2019, 2019
Short summary
Short summary
Human activities strongly interfere in the land–atmosphere interactions through changes in land use and land cover changes and land management. The objectives of this review are to synthesize the existing experimental and modelling works that investigate physical, chemical, and biogeochemical interactions between land surface and the atmosphere. Greater consideration of atmospheric chemistry, through land–atmosphere interactions, as a decision parameter for land management is essential.
Benjamin Loubet, Marco Carozzi, Polina Voylokov, Jean-Pierre Cohan, Robert Trochard, and Sophie Génermont
Biogeosciences, 15, 3439–3460, https://doi.org/10.5194/bg-15-3439-2018, https://doi.org/10.5194/bg-15-3439-2018, 2018
Short summary
Short summary
Tropospheric ammonia is mainly emitted by agriculture. It constitutes a loss for the farmers and a threat to human health and the environment. It is therefore crucial to improve agricultural practices to reduce ammonia losses following fertilisation. In this study we propose an inverse dispersion modelling method to simultaneously quantify ammonia volatilisation from multiple small agronomic plots. The method was evaluated to be suitable (though slightly biased) based on a theoretical study.
C. R. Flechard, R.-S. Massad, B. Loubet, E. Personne, D. Simpson, J. O. Bash, E. J. Cooter, E. Nemitz, and M. A. Sutton
Biogeosciences, 10, 5183–5225, https://doi.org/10.5194/bg-10-5183-2013, https://doi.org/10.5194/bg-10-5183-2013, 2013
Related subject area
Biogeochemistry: Modelling, Terrestrial
Future projections of Siberian wildfire and aerosol emissions
Mechanisms of soil organic carbon and nitrogen stabilization in mineral-associated organic matter – insights from modeling in phase space
Optimizing the terrestrial ecosystem gross primary productivity using carbonyl sulfide (COS) within a two-leaf modeling framework
Modeling integrated soil fertility management for maize production in Kenya using a Bayesian calibration of the DayCent model
When and why microbial-explicit soil organic carbon models can be unstable
The impacts of modelling prescribed vs. dynamic land cover in a high-CO2 future scenario – greening of the Arctic and Amazonian dieback
Climate-based prediction of carbon fluxes from deadwood in Australia
Integration of tree hydraulic processes and functional impairment to capture the drought resilience of a semiarid pine forest
The effect of temperature on photosystem II efficiency across plant functional types and climate
Modeling microbial carbon fluxes and stocks in global soils from 1901 to 2016
Elevated atmospheric CO2 concentration and vegetation structural changes contributed to gross primary productivity increase more than climate and forest cover changes in subtropical forests of China
Non-steady-state stomatal conductance modeling and its implications: from leaf to ecosystem
Modelled forest ecosystem carbon–nitrogen dynamics with integrated mycorrhizal processes under elevated CO2
A chemical kinetics theory for interpreting the non-monotonic temperature dependence of enzymatic reactions
Using Free Air CO2 Enrichment data to constrain land surface model projections of the terrestrial carbon cycle
Multiscale assessment of North American terrestrial carbon balance
Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site
2001–2022 global gross primary productivity dataset using an ensemble model based on random forest
X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X
Spatial biases reduce the ability of Earth system models to simulate soil heterotrophic respiration fluxes
Tropical dry forest response to nutrient fertilization: a model validation and sensitivity analysis
Connecting competitor, stress-tolerator and ruderal (CSR) theory and Lund Potsdam Jena managed Land 5 (LPJmL 5) to assess the role of environmental conditions, management and functional diversity for grassland ecosystem functions
A global fuel characteristic model and dataset for wildfire prediction
Can models adequately reflect how long-term nitrogen enrichment alters the forest soil carbon cycle?
Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers
Does dynamically modelled leaf area improve predictions of land surface water and carbon fluxes? – Insights into dynamic vegetation modules
Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia
A modeling approach to investigate drivers, variability and uncertainties in O2 fluxes and O2 : CO2 exchange ratios in a temperate forest
Modeling coupled nitrification–denitrification in soil with an organic hotspot
A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland
Enabling a process-oriented hydro-biogeochemical model to simulate soil erosion and nutrient losses
Potassium limitation of forest productivity – Part 1: A mechanistic model simulating the effects of potassium availability on canopy carbon and water fluxes in tropical eucalypt stands
Potassium limitation of forest productivity – Part 2: CASTANEA-MAESPA-K shows a reduction in photosynthesis rather than a stoichiometric limitation of tissue formation
Global evaluation of terrestrial biogeochemistry in the Energy Exascale Earth System Model (E3SM) and the role of the phosphorus cycle in the historical terrestrial carbon balance
Assessing carbon storage capacity and saturation across six central US grasslands using data–model integration
Optimizing the carbonic anhydrase temperature response and stomatal conductance of carbonyl sulfide leaf uptake in the Simple Biosphere model (SiB4)
Exploring environmental and physiological drivers of the annual carbon budget of biocrusts from various climatic zones with a mechanistic data-driven model
Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance
Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model
Exploring the impacts of unprecedented climate extremes on forest ecosystems: hypotheses to guide modeling and experimental studies
Effect of droughts and climate change on future soil weathering rates in Sweden
Information content in time series of litter decomposition studies and the transit time of litter in arid lands
Long-term changes of nitrogen leaching and the contributions of terrestrial nutrient sources to lake eutrophication dynamics on the Yangtze Plain of China
Towards an ensemble-based evaluation of land surface models in light of uncertain forcings and observations
Observational benchmarks inform representation of soil organic carbon dynamics in land surface models
Effect of land-use legacy on the future carbon sink for the conterminous US
Peatlands and their carbon dynamics in northern high latitudes from 1990 to 2300: a process-based biogeochemistry model analysis
Improved representation of phosphorus exchange on soil mineral surfaces reduces estimates of phosphorus limitation in temperate forest ecosystems
A coupled ground heat flux–surface energy balance model of evaporation using thermal remote sensing observations
Modeling nitrous oxide emissions from agricultural soil incubation experiments using CoupModel
Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata
Biogeosciences, 21, 4195–4227, https://doi.org/10.5194/bg-21-4195-2024, https://doi.org/10.5194/bg-21-4195-2024, 2024
Short summary
Short summary
SPITFIRE (SPread and InTensity of FIRE) was integrated into a spatially explicit individual-based dynamic global vegetation model to improve the accuracy of depicting Siberian forest fire frequency, intensity, and extent. Fires showed increased greenhouse gas and aerosol emissions in 2006–2100 for Representative Concentration Pathways. This study contributes to understanding fire dynamics, land ecosystem–climate interactions, and global material cycles under the threat of escalating fires.
Stefano Manzoni and M. Francesca Cotrufo
Biogeosciences, 21, 4077–4098, https://doi.org/10.5194/bg-21-4077-2024, https://doi.org/10.5194/bg-21-4077-2024, 2024
Short summary
Short summary
Organic carbon and nitrogen are stabilized in soils via microbial assimilation and stabilization of necromass (in vivo pathway) or via adsorption of the products of extracellular decomposition (ex vivo pathway). Here we use a diagnostic model to quantify which stabilization pathway is prevalent using data on residue-derived carbon and nitrogen incorporation in mineral-associated organic matter. We find that the in vivo pathway is dominant in fine-textured soils with low organic matter content.
Huajie Zhu, Xiuli Xing, Mousong Wu, Weimin Ju, and Fei Jiang
Biogeosciences, 21, 3735–3760, https://doi.org/10.5194/bg-21-3735-2024, https://doi.org/10.5194/bg-21-3735-2024, 2024
Short summary
Short summary
Ecosystem carbonyl sulfide (COS) fluxes were employed to optimize GPP estimation across ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was developed for simulating the canopy COS uptake under its state-of-the-art two-leaf modeling framework. Our results showcased the efficacy of COS in improving model prediction and reducing prediction uncertainty of GPP and enhanced insights into the sensitivity, identifiability, and interactions of parameters related to COS.
Moritz Laub, Magdalena Necpalova, Marijn Van de Broek, Marc Corbeels, Samuel Mathu Ndungu, Monicah Wanjiku Mucheru-Muna, Daniel Mugendi, Rebecca Yegon, Wycliffe Waswa, Bernard Vanlauwe, and Johan Six
Biogeosciences, 21, 3691–3716, https://doi.org/10.5194/bg-21-3691-2024, https://doi.org/10.5194/bg-21-3691-2024, 2024
Short summary
Short summary
We used the DayCent model to assess the potential impact of integrated soil fertility management (ISFM) on maize production, soil fertility, and greenhouse gas emission in Kenya. After adjustments, DayCent represented measured mean yields and soil carbon stock changes well and N2O emissions acceptably. Our results showed that soil fertility losses could be reduced but not completely eliminated with ISFM and that, while N2O emissions increased with ISFM, emissions per kilogram yield decreased.
Erik Schwarz, Samia Ghersheen, Salim Belyazid, and Stefano Manzoni
Biogeosciences, 21, 3441–3461, https://doi.org/10.5194/bg-21-3441-2024, https://doi.org/10.5194/bg-21-3441-2024, 2024
Short summary
Short summary
The occurrence of unstable equilibrium points (EPs) could impede the applicability of microbial-explicit soil organic carbon models. For archetypal model versions we identify when instability can occur and describe mathematical conditions to avoid such unstable EPs. We discuss implications for further model development, highlighting the important role of considering basic ecological principles to ensure biologically meaningful models.
Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, and Libo Wang
Biogeosciences, 21, 3339–3371, https://doi.org/10.5194/bg-21-3339-2024, https://doi.org/10.5194/bg-21-3339-2024, 2024
Short summary
Short summary
Terrestrial biosphere models can either prescribe the geographical distribution of biomes or simulate them dynamically, capturing climate-change-driven biome shifts. We isolate and examine the differences between these different land cover implementations. We find that the simulated terrestrial carbon sink at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover due to important range shifts in the Arctic and Amazon.
Elizabeth S. Duan, Luciana Chavez Rodriguez, Nicole Hemming-Schroeder, Baptiste Wijas, Habacuc Flores-Moreno, Alexander W. Cheesman, Lucas A. Cernusak, Michael J. Liddell, Paul Eggleton, Amy E. Zanne, and Steven D. Allison
Biogeosciences, 21, 3321–3338, https://doi.org/10.5194/bg-21-3321-2024, https://doi.org/10.5194/bg-21-3321-2024, 2024
Short summary
Short summary
Understanding the link between climate and carbon fluxes is crucial for predicting how climate change will impact carbon sinks. We estimated carbon dioxide (CO2) fluxes from deadwood in tropical Australia using wood moisture content and temperature. Our model predicted that the majority of deadwood carbon is released as CO2, except when termite activity is detected. Future models should also incorporate wood traits, like species and chemical composition, to better predict fluxes.
Daniel Nadal-Sala, Rüdiger Grote, David Kraus, Uri Hochberg, Tamir Klein, Yael Wagner, Fedor Tatarinov, Dan Yakir, and Nadine K. Ruehr
Biogeosciences, 21, 2973–2994, https://doi.org/10.5194/bg-21-2973-2024, https://doi.org/10.5194/bg-21-2973-2024, 2024
Short summary
Short summary
A hydraulic model approach is presented that can be added to any physiologically based ecosystem model. Simulated plant water potential triggers stomatal closure, photosynthesis decline, root–soil resistance increases, and sapwood and foliage senescence. The model has been evaluated at an extremely dry site stocked with Aleppo pine and was able to represent gas exchange, soil water content, and plant water potential. The model also responded realistically regarding leaf senescence.
Patrick Neri, Lianhong Gu, and Yang Song
Biogeosciences, 21, 2731–2758, https://doi.org/10.5194/bg-21-2731-2024, https://doi.org/10.5194/bg-21-2731-2024, 2024
Short summary
Short summary
A first-of-its-kind global-scale model of temperature resilience and tolerance of photosystem II maximum quantum yield informs how plants maintain their efficiency of converting light energy to chemical energy for photosynthesis under temperature changes. Our finding explores this variation across plant functional types and habitat climatology, highlighting diverse temperature response strategies and a method to improve global-scale photosynthesis modeling under climate change.
Liyuan He, Jorge L. Mazza Rodrigues, Melanie A. Mayes, Chun-Ta Lai, David A. Lipson, and Xiaofeng Xu
Biogeosciences, 21, 2313–2333, https://doi.org/10.5194/bg-21-2313-2024, https://doi.org/10.5194/bg-21-2313-2024, 2024
Short summary
Short summary
Soil microbes are the driving engine for biogeochemical cycles of carbon and nutrients. This study applies a microbial-explicit model to quantify bacteria and fungal biomass carbon in soils from 1901 to 2016. Results showed substantial increases in bacterial and fungal biomass carbon over the past century, jointly influenced by vegetation growth and soil temperature and moisture. This pioneering century-long estimation offers crucial insights into soil microbial roles in global carbon cycling.
Tao Chen, Félicien Meunier, Marc Peaucelle, Guoping Tang, Ye Yuan, and Hans Verbeeck
Biogeosciences, 21, 2253–2272, https://doi.org/10.5194/bg-21-2253-2024, https://doi.org/10.5194/bg-21-2253-2024, 2024
Short summary
Short summary
Chinese subtropical forest ecosystems are an extremely important component of global forest ecosystems and hence crucial for the global carbon cycle and regional climate change. However, there is still great uncertainty in the relationship between subtropical forest carbon sequestration and its drivers. We provide first quantitative estimates of the individual and interactive effects of different drivers on the gross primary productivity changes of various subtropical forest types in China.
Ke Liu, Yujie Wang, Troy S. Magney, and Christian Frankenberg
Biogeosciences, 21, 1501–1516, https://doi.org/10.5194/bg-21-1501-2024, https://doi.org/10.5194/bg-21-1501-2024, 2024
Short summary
Short summary
Stomata are pores on leaves that regulate gas exchange between plants and the atmosphere. Existing land models unrealistically assume stomata can jump between steady states when the environment changes. We implemented dynamic modeling to predict gradual stomatal responses at different scales. Results suggested that considering this effect on plant behavior patterns in diurnal cycles was important. Our framework also simplified simulations and can contribute to further efficiency improvements.
Melanie A. Thurner, Silvia Caldararu, Jan Engel, Anja Rammig, and Sönke Zaehle
Biogeosciences, 21, 1391–1410, https://doi.org/10.5194/bg-21-1391-2024, https://doi.org/10.5194/bg-21-1391-2024, 2024
Short summary
Short summary
Due to their crucial role in terrestrial ecosystems, we implemented mycorrhizal fungi into the QUINCY terrestrial biosphere model. Fungi interact with mineral and organic soil to support plant N uptake and, thus, plant growth. Our results suggest that the effect of mycorrhizal interactions on simulated ecosystem dynamics is minor under constant environmental conditions but necessary to reproduce and understand observed patterns under changing conditions, such as rising atmospheric CO2.
Jinyun Tang and William J. Riley
Biogeosciences, 21, 1061–1070, https://doi.org/10.5194/bg-21-1061-2024, https://doi.org/10.5194/bg-21-1061-2024, 2024
Short summary
Short summary
A chemical kinetics theory is proposed to explain the non-monotonic relationship between temperature and biochemical rates. It incorporates the observed thermally reversible enzyme denaturation that is ensured by the ceaseless thermal motion of molecules and ions in an enzyme solution and three well-established theories: (1) law of mass action, (2) diffusion-limited chemical reaction theory, and (3) transition state theory.
Nina Raoult, Louis-Axel Edouard-Rambaut, Nicolas Vuichard, Vladislav Bastrikov, Anne Sofie Lansø, Bertrand Guenet, and Philippe Peylin
Biogeosciences, 21, 1017–1036, https://doi.org/10.5194/bg-21-1017-2024, https://doi.org/10.5194/bg-21-1017-2024, 2024
Short summary
Short summary
Observations are used to reduce uncertainty in land surface models (LSMs) by optimising poorly constraining parameters. However, optimising against current conditions does not necessarily ensure that the parameters treated as invariant will be robust in a changing climate. Manipulation experiments offer us a unique chance to optimise our models under different (here atmospheric CO2) conditions. By using these data in optimisations, we gain confidence in the future projections of LSMs.
Kelsey T. Foster, Wu Sun, Yoichi P. Shiga, Jiafu Mao, and Anna M. Michalak
Biogeosciences, 21, 869–891, https://doi.org/10.5194/bg-21-869-2024, https://doi.org/10.5194/bg-21-869-2024, 2024
Short summary
Short summary
Assessing agreement between bottom-up and top-down methods across spatial scales can provide insights into the relationship between ensemble spread (difference across models) and model accuracy (difference between model estimates and reality). We find that ensemble spread is unlikely to be a good indicator of actual uncertainty in the North American carbon balance. However, models that are consistent with atmospheric constraints show stronger agreement between top-down and bottom-up estimates.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
Short summary
Short summary
We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Xin Chen, Tiexi Chen, Xiaodong Li, Yuanfang Chai, Shengjie Zhou, Renjie Guo, and Jie Dai
EGUsphere, https://doi.org/10.5194/egusphere-2024-114, https://doi.org/10.5194/egusphere-2024-114, 2024
Short summary
Short summary
We provides an ensemble model-based GPP dataset (ERF_GPP) that explains 83.7 % of the monthly variation in GPP across 171 sites, higher than other single remote sensing model. In addition, ERF_GPP improves the phenomenon of “high value underestimation and low value overestimation” in GPP estimation to some extent. Overall, ERF_GPP provides a more reliable estimate of global GPP and will facilitate further development of carbon cycle research.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billdesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Gharun Mana, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
EGUsphere, https://doi.org/10.5194/egusphere-2024-165, https://doi.org/10.5194/egusphere-2024-165, 2024
Short summary
Short summary
The movement of water, carbon, and energy from the earth surface to the atmosphere, or flux, is an important process to understand that impacts all of our lives. Here we outline a method to estimate global water and CO2 fluxes based on direct measurements from site around the world called FLUXCOM-X. We go on to demonstrate how these new estimates of net CO2 uptake/loss, gross CO2 uptake, total water evaporation, and transpiration from plants compare to previous and independent estimates.
Bertrand Guenet, Jérémie Orliac, Lauric Cécillon, Olivier Torres, Laura Sereni, Philip A. Martin, Pierre Barré, and Laurent Bopp
Biogeosciences, 21, 657–669, https://doi.org/10.5194/bg-21-657-2024, https://doi.org/10.5194/bg-21-657-2024, 2024
Short summary
Short summary
Heterotrophic respiration fluxes are a major flux between surfaces and the atmosphere, but Earth system models do not yet represent them correctly. Here we benchmarked Earth system models against observation-based products, and we identified the important mechanisms that need to be improved in the next-generation Earth system models.
Shuyue Li, Bonnie Waring, Jennifer Powers, and David Medvigy
Biogeosciences, 21, 455–471, https://doi.org/10.5194/bg-21-455-2024, https://doi.org/10.5194/bg-21-455-2024, 2024
Short summary
Short summary
We used an ecosystem model to simulate primary production of a tropical forest subjected to 3 years of nutrient fertilization. Simulations parameterized such that relative allocation to fine roots increased with increasing soil phosphorus had leaf, wood, and fine root production consistent with observations. However, these simulations seemed to over-allocate to fine roots on multidecadal timescales, affecting aboveground biomass. Additional observations across timescales would benefit models.
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.
Joe R. McNorton and Francesca Di Giuseppe
Biogeosciences, 21, 279–300, https://doi.org/10.5194/bg-21-279-2024, https://doi.org/10.5194/bg-21-279-2024, 2024
Short summary
Short summary
Wildfires have wide-ranging consequences for local communities, air quality and ecosystems. Vegetation amount and moisture state are key components to forecast wildfires. We developed a combined model and satellite framework to characterise vegetation, including the type of fuel, whether it is alive or dead, and its moisture content. The daily data is at high resolution globally (~9 km). Our characteristics correlate with active fire data and can inform fire danger and spread modelling efforts.
Brooke A. Eastman, William R. Wieder, Melannie D. Hartman, Edward R. Brzostek, and William T. Peterjohn
Biogeosciences, 21, 201–221, https://doi.org/10.5194/bg-21-201-2024, https://doi.org/10.5194/bg-21-201-2024, 2024
Short summary
Short summary
We compared soil model performance to data from a long-term nitrogen addition experiment in a forested ecosystem. We found that in order for soil carbon models to accurately predict future forest carbon sequestration, two key processes must respond dynamically to nitrogen availability: (1) plant allocation of carbon to wood versus roots and (2) rates of soil organic matter decomposition. Long-term experiments can help improve our predictions of the land carbon sink and its climate impact.
Jan De Pue, Sebastian Wieneke, Ana Bastos, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Maral Maleki, Fabienne Maignan, Françoise Gellens-Meulenberghs, Ivan Janssens, and Manuela Balzarolo
Biogeosciences, 20, 4795–4818, https://doi.org/10.5194/bg-20-4795-2023, https://doi.org/10.5194/bg-20-4795-2023, 2023
Short summary
Short summary
The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. To estimate this flux, models can rely on remote sensing data (RS-driven), meteorological data (meteo-driven) or a combination of both (hybrid). An intercomparison of 11 models demonstrated that RS-driven models lack the sensitivity to short-term anomalies. Conversely, the simulation of soil moisture dynamics and stress response remains a challenge in meteo-driven models.
Sven Armin Westermann, Anke Hildebrandt, Souhail Bousetta, and Stephan Thober
EGUsphere, https://doi.org/10.5194/egusphere-2023-2101, https://doi.org/10.5194/egusphere-2023-2101, 2023
Short summary
Short summary
Plants at the land surface mediates between soil and atmosphere regarding water and carbon transport. Since plant growth is a dynamic process, models need to care for this dynamics. Here, two models which predict water and carbon fluxes by considering plant temporal evolution were tested against observational data. Currently, dynamizing plants in these models did not enhance their representativeness which is caused by a mismatch between implemented physical relations and observable connections.
Chad A. Burton, Luigi J. Renzullo, Sami W. Rifai, and Albert I. J. M. Van Dijk
Biogeosciences, 20, 4109–4134, https://doi.org/10.5194/bg-20-4109-2023, https://doi.org/10.5194/bg-20-4109-2023, 2023
Short summary
Short summary
Australia's land-based ecosystems play a critical role in controlling the variability in the global land carbon sink. However, uncertainties in the methods used for quantifying carbon fluxes limit our understanding. We develop high-resolution estimates of Australia's land carbon fluxes using machine learning methods and find that Australia is, on average, a stronger carbon sink than previously thought and that the seasonal dynamics of the fluxes differ from those described by other methods.
Yuan Yan, Anne Klosterhalfen, Fernando Moyano, Matthias Cuntz, Andrew C. Manning, and Alexander Knohl
Biogeosciences, 20, 4087–4107, https://doi.org/10.5194/bg-20-4087-2023, https://doi.org/10.5194/bg-20-4087-2023, 2023
Short summary
Short summary
A better understanding of O2 fluxes, their exchange ratios with CO2 and their interrelations with environmental conditions would provide further insights into biogeochemical ecosystem processes. We, therefore, used the multilayer canopy model CANVEG to simulate and analyze the flux exchange for our forest study site for 2012–2016. Based on these simulations, we further successfully tested the application of various micrometeorological methods and the prospects of real O2 flux measurements.
Jie Zhang, Elisabeth Larsen Kolstad, Wenxin Zhang, Iris Vogeler, and Søren O. Petersen
Biogeosciences, 20, 3895–3917, https://doi.org/10.5194/bg-20-3895-2023, https://doi.org/10.5194/bg-20-3895-2023, 2023
Short summary
Short summary
Manure application to agricultural land often results in large and variable N2O emissions. We propose a model with a parsimonious structure to investigate N transformations around such N2O hotspots. The model allows for new detailed insights into the interactions between transport and microbial activities regarding N2O emissions in heterogeneous soil environments. It highlights the importance of solute diffusion to N2O emissions from such hotspots which are often ignored by process-based models.
Jukka Alm, Antti Wall, Jukka-Pekka Myllykangas, Paavo Ojanen, Juha Heikkinen, Helena M. Henttonen, Raija Laiho, Kari Minkkinen, Tarja Tuomainen, and Juha Mikola
Biogeosciences, 20, 3827–3855, https://doi.org/10.5194/bg-20-3827-2023, https://doi.org/10.5194/bg-20-3827-2023, 2023
Short summary
Short summary
In Finland peatlands cover one-third of land area. For half of those, with 4.3 Mha being drained for forestry, Finland reports sinks and sources of greenhouse gases in forest lands on organic soils following its UNFCCC commitment. We describe a new method for compiling soil CO2 balance that follows changes in tree volume, tree harvests and temperature. An increasing trend of emissions from 1.4 to 7.9 Mt CO2 was calculated for drained peatland forest soils in Finland for 1990–2021.
Siqi Li, Bo Zhu, Xunhua Zheng, Pengcheng Hu, Shenghui Han, Jihui Fan, Tao Wang, Rui Wang, Kai Wang, Zhisheng Yao, Chunyan Liu, Wei Zhang, and Yong Li
Biogeosciences, 20, 3555–3572, https://doi.org/10.5194/bg-20-3555-2023, https://doi.org/10.5194/bg-20-3555-2023, 2023
Short summary
Short summary
Physical soil erosion and particulate carbon, nitrogen and phosphorus loss modules were incorporated into the process-oriented hydro-biogeochemical model CNMM-DNDC to realize the accurate simulation of water-induced erosion and subsequent particulate nutrient losses at high spatiotemporal resolution.
Ivan Cornut, Nicolas Delpierre, Jean-Paul Laclau, Joannès Guillemot, Yann Nouvellon, Otavio Campoe, Jose Luiz Stape, Vitoria Fernanda Santos, and Guerric le Maire
Biogeosciences, 20, 3093–3117, https://doi.org/10.5194/bg-20-3093-2023, https://doi.org/10.5194/bg-20-3093-2023, 2023
Short summary
Short summary
Potassium is an essential element for living organisms. Trees are dependent upon this element for certain functions that allow them to build their trunks using carbon dioxide. Using data from experiments in eucalypt plantations in Brazil and a simplified computer model of the plantations, we were able to investigate the effect that a lack of potassium can have on the production of wood. Understanding nutrient cycles is useful to understand the response of forests to environmental change.
Ivan Cornut, Guerric le Maire, Jean-Paul Laclau, Joannès Guillemot, Yann Nouvellon, and Nicolas Delpierre
Biogeosciences, 20, 3119–3135, https://doi.org/10.5194/bg-20-3119-2023, https://doi.org/10.5194/bg-20-3119-2023, 2023
Short summary
Short summary
After simulating the effects of low levels of potassium on the canopy of trees and the uptake of carbon dioxide from the atmosphere by leaves in Part 1, here we tried to simulate the way the trees use the carbon they have acquired and the interaction with the potassium cycle in the tree. We show that the effect of low potassium on the efficiency of the trees in acquiring carbon is enough to explain why they produce less wood when they are in soils with low levels of potassium.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
Short summary
Short summary
We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Kevin R. Wilcox, Scott L. Collins, Alan K. Knapp, William Pockman, Zheng Shi, Melinda D. Smith, and Yiqi Luo
Biogeosciences, 20, 2707–2725, https://doi.org/10.5194/bg-20-2707-2023, https://doi.org/10.5194/bg-20-2707-2023, 2023
Short summary
Short summary
The capacity for carbon storage (C capacity) is an attribute that determines how ecosystems store carbon in the future. Here, we employ novel data–model integration techniques to identify the carbon capacity of six grassland sites spanning the US Great Plains. Hot and dry sites had low C capacity due to less plant growth and high turnover of soil C, so they may be a C source in the future. Alternately, cooler and wetter ecosystems had high C capacity, so these systems may be a future C sink.
Ara Cho, Linda M. J. Kooijmans, Kukka-Maaria Kohonen, Richard Wehr, and Maarten C. Krol
Biogeosciences, 20, 2573–2594, https://doi.org/10.5194/bg-20-2573-2023, https://doi.org/10.5194/bg-20-2573-2023, 2023
Short summary
Short summary
Carbonyl sulfide (COS) is a useful constraint for estimating photosynthesis. To simulate COS leaf flux better in the SiB4 model, we propose a novel temperature function for enzyme carbonic anhydrase (CA) activity and optimize conductances using observations. The optimal activity of CA occurs below 40 °C, and Ball–Woodrow–Berry parameters are slightly changed. These reduce/increase uptakes in the tropics/higher latitudes and contribute to resolving discrepancies in the COS global budget.
Yunyao Ma, Bettina Weber, Alexandra Kratz, José Raggio, Claudia Colesie, Maik Veste, Maaike Y. Bader, and Philipp Porada
Biogeosciences, 20, 2553–2572, https://doi.org/10.5194/bg-20-2553-2023, https://doi.org/10.5194/bg-20-2553-2023, 2023
Short summary
Short summary
We found that the modelled annual carbon balance of biocrusts is strongly affected by both the environment (mostly air temperature and CO2 concentration) and physiology, such as temperature response of respiration. However, the relative impacts of these drivers vary across regions with different climates. Uncertainty in driving factors may lead to unrealistic carbon balance estimates, particularly in temperate climates, and may be explained by seasonal variation of physiology due to acclimation.
Alexander J. Norton, A. Anthony Bloom, Nicholas C. Parazoo, Paul A. Levine, Shuang Ma, Renato K. Braghiere, and T. Luke Smallman
Biogeosciences, 20, 2455–2484, https://doi.org/10.5194/bg-20-2455-2023, https://doi.org/10.5194/bg-20-2455-2023, 2023
Short summary
Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.
Libo Wang, Vivek K. Arora, Paul Bartlett, Ed Chan, and Salvatore R. Curasi
Biogeosciences, 20, 2265–2282, https://doi.org/10.5194/bg-20-2265-2023, https://doi.org/10.5194/bg-20-2265-2023, 2023
Short summary
Short summary
Plant functional types (PFTs) are groups of plant species used to represent vegetation distribution in land surface models. There are large uncertainties associated with existing methods for mapping land cover datasets to PFTs. This study demonstrates how fine-resolution tree cover fraction and land cover datasets can be used to inform the PFT mapping process and reduce the uncertainties. The proposed largely objective method makes it easier to implement new land cover products in models.
Jennifer A. Holm, David M. Medvigy, Benjamin Smith, Jeffrey S. Dukes, Claus Beier, Mikhail Mishurov, Xiangtao Xu, Jeremy W. Lichstein, Craig D. Allen, Klaus S. Larsen, Yiqi Luo, Cari Ficken, William T. Pockman, William R. L. Anderegg, and Anja Rammig
Biogeosciences, 20, 2117–2142, https://doi.org/10.5194/bg-20-2117-2023, https://doi.org/10.5194/bg-20-2117-2023, 2023
Short summary
Short summary
Unprecedented climate extremes (UCEs) are expected to have dramatic impacts on ecosystems. We present a road map of how dynamic vegetation models can explore extreme drought and climate change and assess ecological processes to measure and reduce model uncertainties. The models predict strong nonlinear responses to UCEs. Due to different model representations, the models differ in magnitude and trajectory of forest loss. Therefore, we explore specific plant responses that reflect knowledge gaps.
Veronika Kronnäs, Klas Lucander, Giuliana Zanchi, Nadja Stadlinger, Salim Belyazid, and Cecilia Akselsson
Biogeosciences, 20, 1879–1899, https://doi.org/10.5194/bg-20-1879-2023, https://doi.org/10.5194/bg-20-1879-2023, 2023
Short summary
Short summary
In a future climate, extreme droughts might become more common. Climate change and droughts can have negative effects on soil weathering and plant health.
In this study, climate change effects on weathering were studied on sites in Sweden using the model ForSAFE, a climate change scenario and an extreme drought scenario. The modelling shows that weathering is higher during summer and increases with global warming but that weathering during drought summers can become as low as winter weathering.
Agustín Sarquis and Carlos A. Sierra
Biogeosciences, 20, 1759–1771, https://doi.org/10.5194/bg-20-1759-2023, https://doi.org/10.5194/bg-20-1759-2023, 2023
Short summary
Short summary
Although plant litter is chemically and physically heterogenous and undergoes multiple transformations, models that represent litter dynamics often ignore this complexity. We used a multi-model inference framework to include information content in litter decomposition datasets and studied the time it takes for litter to decompose as measured by the transit time. In arid lands, the median transit time of litter is about 3 years and has a negative correlation with mean annual temperature.
Qi Guan, Jing Tang, Lian Feng, Stefan Olin, and Guy Schurgers
Biogeosciences, 20, 1635–1648, https://doi.org/10.5194/bg-20-1635-2023, https://doi.org/10.5194/bg-20-1635-2023, 2023
Short summary
Short summary
Understanding terrestrial sources of nitrogen is vital to examine lake eutrophication changes. Combining process-based ecosystem modeling and satellite observations, we found that land-leached nitrogen in the Yangtze Plain significantly increased from 1979 to 2018, and terrestrial nutrient sources were positively correlated with eutrophication trends observed in most lakes, demonstrating the necessity of sustainable nitrogen management to control eutrophication.
Vivek K. Arora, Christian Seiler, Libo Wang, and Sian Kou-Giesbrecht
Biogeosciences, 20, 1313–1355, https://doi.org/10.5194/bg-20-1313-2023, https://doi.org/10.5194/bg-20-1313-2023, 2023
Short summary
Short summary
The behaviour of natural systems is now very often represented through mathematical models. These models represent our understanding of how nature works. Of course, nature does not care about our understanding. Since our understanding is not perfect, evaluating models is challenging, and there are uncertainties. This paper illustrates this uncertainty for land models and argues that evaluating models in light of the uncertainty in various components provides useful information.
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-50, https://doi.org/10.5194/bg-2023-50, 2023
Revised manuscript accepted for BG
Short summary
Short summary
Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. We used machine learning to develop and compare predictive relationships in observations and ESMs. We found different relationships between environmental factors and SOC stocks in observations and ESMs. SOC predictions in ESMs may be improved by representing the functional relationships of environmental controllers consistent with observations.
Benjamin S. Felzer
Biogeosciences, 20, 573–587, https://doi.org/10.5194/bg-20-573-2023, https://doi.org/10.5194/bg-20-573-2023, 2023
Short summary
Short summary
The future of the terrestrial carbon sink depends upon the legacy of past land use, which determines the stand age of the forest and nutrient levels in the soil, both of which affect vegetation growth. This study uses a modeling approach to determine the effects of land-use legacy in the conterminous US from 1750 to 2099. Not accounting for land legacy results in a low carbon sink and high biomass, while water variables are not as highly affected.
Bailu Zhao and Qianlai Zhuang
Biogeosciences, 20, 251–270, https://doi.org/10.5194/bg-20-251-2023, https://doi.org/10.5194/bg-20-251-2023, 2023
Short summary
Short summary
In this study, we use a process-based model to simulate the northern peatland's C dynamics in response to future climate change during 1990–2300. Northern peatlands are projected to be a C source under all climate scenarios except for the mildest one before 2100 and C sources under all scenarios afterwards.
We find northern peatlands are a C sink until pan-Arctic annual temperature reaches −2.09 to −2.89 °C. This study emphasizes the vulnerability of northern peatlands to climate change.
Lin Yu, Silvia Caldararu, Bernhard Ahrens, Thomas Wutzler, Marion Schrumpf, Julian Helfenstein, Chiara Pistocchi, and Sönke Zaehle
Biogeosciences, 20, 57–73, https://doi.org/10.5194/bg-20-57-2023, https://doi.org/10.5194/bg-20-57-2023, 2023
Short summary
Short summary
In this study, we addressed a key weakness in current ecosystem models regarding the phosphorus exchange in the soil and developed a new scheme to describe this process. We showed that the new scheme improved the model performance for plant productivity, soil organic carbon, and soil phosphorus content at five beech forest sites in Germany. We claim that this new model could be used as a better tool to study ecosystems under future climate change, particularly phosphorus-limited systems.
Bimal K. Bhattacharya, Kaniska Mallick, Devansh Desai, Ganapati S. Bhat, Ross Morrison, Jamie R. Clevery, William Woodgate, Jason Beringer, Kerry Cawse-Nicholson, Siyan Ma, Joseph Verfaillie, and Dennis Baldocchi
Biogeosciences, 19, 5521–5551, https://doi.org/10.5194/bg-19-5521-2022, https://doi.org/10.5194/bg-19-5521-2022, 2022
Short summary
Short summary
Evaporation retrieval in heterogeneous ecosystems is challenging due to empirical estimation of ground heat flux and complex parameterizations of conductances. We developed a parameter-sparse coupled ground heat flux-evaporation model and tested it across different limits of water stress and vegetation fraction in the Northern/Southern Hemisphere. The model performed particularly well in the savannas and showed good potential for evaporative stress monitoring from thermal infrared satellites.
Jie Zhang, Wenxin Zhang, Per-Erik Jansson, and Søren O. Petersen
Biogeosciences, 19, 4811–4832, https://doi.org/10.5194/bg-19-4811-2022, https://doi.org/10.5194/bg-19-4811-2022, 2022
Short summary
Short summary
In this study, we relied on a properly controlled laboratory experiment to test the model’s capability of simulating the dominant microbial processes and the emissions of one greenhouse gas (nitrous oxide, N2O) from agricultural soils. This study reveals important processes and parameters that regulate N2O emissions in the investigated model framework and also suggests future steps of model development, which have implications on the broader communities of ecosystem modelers.
Cited articles
Aguilera, E., Lassaletta, L., Sanz Cobeña, A., Garnier, J., and Vallejo
Garcia, A.: The potential of organic fertilizers and water management to
reduce N2O emissions in Mediterranean climate cropping systems, Agr.
Ecosyst. Environ., 164, 32–52,
https://doi.org/10.1016/j.agee.2012.09.006, 2013.
Ammann, C., Neftel, A., Jocher, M., Fuhrer, J., and Leifeld, J.: Effect of
management and weather variations on the greenhouse gas budget of two
grasslands during a 10-year experiment, Agr. Ecosyst. Environ., 292, 106814,
https://doi.org/10.1016/j.agee.2019.106814, 2020.
Amundson, R. and Biardeau, L.: Opinion: Soil carbon sequestration is an
elusive climate mitigation tool, P. Natl. Acad. Sci. USA, 115,
11652–11656, https://doi.org/10.1073/pnas.1815901115, 2018.
Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B.,
Cammarano, D., Kimball, B. A., Ottman, M. J., Wall, G. W., White, J. W., and
Reynolds, M. P.: Rising temperatures reduce global wheat production, Nat. Clim.
Change, 5, 143–147, https://doi.org/10.1038/nclimate2470,
2015.
Bahn, M., Rodeghiero, M., Anderson-Dunn, M., Dore, S., Gimeno, C.,
Drösler, M., Williams, M., Ammann, C., Berninger, F., Flechard, C., and
Jones, S.: Soil respiration in European grasslands in relation to climate
and assimilate supply, Ecosystems, 11, 1352–1367, https://doi.org/10.1007/s10021-008-9198-0, 2008.
Balkovič, J., van der Velde, M., Schmid, E., Skalský, R., Khabarov,
N., Obersteiner, M, Stürmer, B., and Xiong, W.: Pan-European crop modelling
with EPIC: implementation, up-scaling and regional crop yield validation,
Agr. Syst., 120, 61–75, https://doi.org/10.1016/j.agsy.2013.05.008, 2013.
Bassu, S., Brisson, N., Durand, J.-L., Boote, K., Lizaso, J., Jones, J.W.,
Rosenzweig, C., Ruane, A. C., Adam, M., Baron, C., Basso, B., Biernath, C.,
Boogaard, H., Conijn, S., Corbeels, M., Deryng, D., De Sanctis, G., Gayler,
S., Grassini, P., Hatfield, J.L., Hoek, S., Izaurralde, C., Jongschaap, R.,
Kemanian, A.R., Kersebaum, K.C., Kim, S.-H., Kumar, N.S., Makowski, D.,
Müller, C., Nendel, C., Priesack, E., Pravia, M.V., Sau, F., Shcherbak,
I., Tao, F., Teixeira, E., Timlin, D., and Waha, K.: How do various maize
crop models vary in their responses to climate change factors?, Glob. Change
Biol., 20, 2301–2320, https://doi.org/10.1111/gcb.12520,
2014.
Blanke, J., Boke-Olén, N., Olin, S., Chang, J., Sahlin, U., Lindeskog,
M., and Lehsten, V.: Implications of accounting for management intensity on
carbon and nitrogen balances of European grasslands, PLoS One, 13,
e0201058, https://doi.org/10.1371/journal.pone.0201058, 2018.
Brilli, L., Bechini, L., Bindi, M., Carozzi, M., Cavalli, D., Conant, R.,
Dorich, C. D., Doro, L., Ehrhardt, F., Farina, R., Ferrise, R., Fitton, N.,
Francaviglia, R., Grace, P., Iocola, I., Klumpp, K., Léonard, J.,
Martin, R., Massad, R. S., Recous, S., Seddaiu, G., Sharp, J., Smith, P.,
Smith, W. N., Soussana, J.-F., and Bellocchi, G.: Review and analysis of
strengths and weaknesses of agro-ecosystem models for simulating C and N
fluxes, Sci. Total Environ., 598, 445–470, https://doi.org/10.1016/j.scitotenv.2017.03.208, 2017.
Britz, W. and Witzke, H.: CAPRI Model Documentation 2008: Version 2,
Institute for Food and Resource Economics, University of Bonn, Germany,
http://www.capri-model.org/docs/capri_documentation.pdf (last access: last access: 17 June 2022), 2008.
Buysse, P., Bodson, B., Debacq, A., De Ligne, A., Heinesch, B., Manise, T.,
Moureaux, C., and Aubinet, M.: Carbon budget measurement over 12 years at a
crop production site in the silty-loam region in Belgium, Agr. Forest
Meteorol., 246, 241–255, https://doi.org/10.1016/j.agrformet.2017.07.004, 2017.
Calanca, P., Vuichard, N., Campbell, C., Viovy, N., Cozic, A., Fuhrer, J.,
and Soussana, J. F.: Simulating the fluxes of CO2 and N2O in European
grasslands with the Pasture Simulation Model (PaSim), Agr. Ecosyst.
Environ., 121, 164–174, https://doi.org/10.1016/j.agee.2006.12.010, 2007.
Cayuela, M. L., Aguilera, E., Sanz-Cobena, A., Adams, D.C., Abalos, D.,
Barton, L., Ryals, R., Silver, W. L., Alfaro, M. A., Pappa, V. A., Smith, P.,
Garnier, J., Billen, G., Bouwman, L., Bondeau, A., and Lassaletta, L.:
Direct nitrous oxide emissions in Mediterranean climate cropping systems:
emission factors based on a meta-analysis of available measurement data,
Agr. Ecosyst. Environ., 238, 25–35, https://doi.org/10.1016/j.agee.2016.10.006, 2017.
Ceschia, E., Beziat, P., Dejoux, J. F., and Aubinet, M.: Management effects on
net ecosystem carbon and GHG budgets at European crop sites, Agriculture,
139, 363–383 , 2010.
Chabbi, A., Lehmann, J., Ciais, P., Loescher, H. W., Cotrufo, M. F., Don,
A., SanClements, M., Schipper, L., Six, J., Smith, P., and Rumpel, C.:
Aligning agriculture and climate policy, Nat. Clim. Change, 7, 307–309,
https://doi.org/10.1038/nclimate3286, 2017.
Challinor, A. J., Ewert, F., Arnold, S., Simelton, E., and Fraser, E.: Crops
and climate change: progress, trends, and challenges in simulating impacts
and informing adaptation, J. Exp. Bot., 60, 2775–2789, https://doi.org/10.1093/jxb/erp062, 2009.
Challinor, A. J., Watson, J., Lobell, D. B., Howden, S. M., Smith, D. R., and
Chhetri, N.: A meta-analysis of crop yield under climate change and
adaptation, Nat. Clim. Change, 4, 287–291, https://doi.org/10.1038/nclimate2153, 2014.
Chang J., Ciais P., Viovy N., Soussana J.-F., Klumpp K., and Sultan B.:
Future productivity and phenology changes in European grasslands for
different warming levels: Implications for grassland management and carbon
balance, Carbon Balance Manag., 12, 1–21, https://doi.org/10.1186/s13021-017-0079-8, 2017.
Chang, J., Viovy, N., Vuichard, N., Ciais, P., Campioli, M., Klumpp, K.,
Martin, R., Leip, A., and Soussana, J.-F.: Modeled changes in potential
grassland productivity and in grass-fed ruminant livestock density in Europe
over 1961–2010, PLoS ONE, 10, e0127554, https://doi.org/10.1371/journal.pone.0127554, 2015.
Chaudhary, A., Gustafson, D., and Mathys, A.: Multi-indicator sustainability
assessment of global food systems, Nat. Commun. 9, 848, https://doi.org/10.1038/s41467-018-03308-7, 2018.
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran,
P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin,
G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire,
A., and Woodward, S.: Development and evaluation of an Earth-System model –
HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011.
Conant, R. T., Cerri, C. E. P., Osborne, B. B., and Paustian, K.: Grassland
management impacts on soil carbon stocks: A new synthesis, Ecol. Appl., 27, 662–668, https://doi.org/10.1002/eap.1473, 2017.
Constantin, J., Raynal, H., Casellas, E., Hoffmann, H., Bindi, M., Doro, L.,
Eckersten, H., Gaiser, T., Grosz, B., Haas, E., Kersebaum, K.-C., Klatt, S.,
Kuhnert, M., Lewan, E., Maharjan, G.R., Moriondo, M., Nendel, C., Roggero,
P.P., Specka, X., Trombi, G., Villa, A., Wang, E., Weihermüller, L.,
Yeluripati, J., Zhao, Z., Ewert, F., and Bergez, J.-E.: Management and
spatial resolution effects on yield and water balance at regional scale in
crop models, Agr. Forest Meteorol., 275, 184–195, https://doi.org/10.1016/j.agrformet.2019.05.013, 2019.
Cowan, N. J., Levy, P. E., Famulari, D., Anderson, M., Drewer, J., Carozzi, M., Reay, D. S., and Skiba, U. M.: The influence of tillage on N2O fluxes from an intensively managed grazed grassland in Scotland, Biogeosciences, 13, 4811–4821, https://doi.org/10.5194/bg-13-4811-2016, 2016.
De Antoni Migliorati, M., Bell, M., Grace, P. R., Scheer, C., Rowlings, D. W.,
and Liu, S.: Legume pastures can reduce N2O emissions intensity in
subtropical cereal cropping systems, Agr. Ecosyst. Environ., 204, 27–39,
https://doi.org/10.1016/j.agee.2015.02.007, 2015.
de Souza, T. T., Silva Antolin, S. L., Bianchini, V., Pereira, R., Moura Silva,
E., and Marin, F.: Longer crop cycle lengths could offset the negative
effects of climate change on Brazilian maize, Bragantia, 78, 622–631,
https://doi.org/10.1590/1678-4499.20190085, 2019.
de Vries, W., Leip, A., Reinds, G. J., Kros, J., Lesschen, J. P., and
Bouwman, A. F.: Comparison of land nitrogen budgets for European agriculture
by various modeling approaches, Environ. Pollut., 159, 3254–3268,
https://doi.org/10.1016/j.envpol.2011.03.038, 2011.
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.
Dono, G., Cortignani, R., Dell'Unto, D., Deligios, P., Doro, L., Lacetera,
N., Mula, L., Pasqui, M., Quaresima, S., Vitali, A., and Roggero, P. P.: Winners
and losers from climate change in agriculture: insights from a case study in
the Mediterranean basin, Agr. Syst., 147, 65–75, https://doi.org/10.1016/j.agsy.2016.05.013, 2016.
Drouet J.-L., Capian N., Fiorelli J.-L., Blanfort V., Capitaine M., Duretz
S., Gabrielle B., Martin R., Lardy R., Cellier P., and Soussana J.-F.:
Sensitivity analysis for models of greenhouse gas emissions at farm level.
Case study of N2O emissions simulated by the CERES-EGC model, Environ.
Pollut., 159, 3156–3161, https://doi.org/10.1016/j.envpol.2011.01.019, 2011.
EC (European Commission): Communication from the commission to the European
parliament, the council, the European economic and social committee and the
committee of the regions Stepping up Europe's 2030 climate ambition
Investing in a climate-neutral future for the benefit of our people,
https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52020DC0562&from=EN (last access: 17 June 2022), 2020.
EEA (European Environment Agency): Trends and projections in Europe 2020,
Tracking progress towards Europe's climate and energy targets, https://www.eea.europa.eu//publications/trends-and-projections-in-europe-2020 (last access: 17 June 2022),
2020.
Ehrhardt, F., Soussana, J.-F., Bellocchi, G., Grace, P., McAuliffe, R.,
Recous, S., Sándor, R., Smith, P., Snow, de Antoni Migliorati, Basso,
B., Bhatia, A., Brilli, L., Doltra, J., Dorich, C. D., Doro, L., Fitton, N.,
Giacomini, S. J., Grant, B., Harrison, M. T., Jones, S. K., Kirschbaum, M.
U. F., Klumpp, K., Laville, P., Léonard, J., Liebig, M., Lieffering,
Martin, R., Massad, R. S., Meier, E., Merbold, L., Moore, Myrgiotis, Newton,
Pattey, Rolinski, S., Sharp, J., Smith, Wu, L., and Zhang, Q.: Assessing
uncertainties in crop and pasture ensemble model simulations of productivity
and N2O emissions, Glob. Change Biol., 24, e603-e616, https://doi.org/10.1111/gcb.13965, 2018.
Emmel, C., Winkler, A., Hörtnagl, L., Revill, A., Ammann, C., D'Odorico,
P., Buchmann, N., and Eugster, W.: Integrated management of a Swiss cropland
is not sufficient to preserve its soil carbon pool in the long term,
Biogeosciences, 15, 5377–5393, https://doi.org/10.5194/bg-15-5377-2018, 2018.
Eurostat: Agri-environmental indicator – greenhouse gas emissions,
https://ec.europa.eu/eurostat/ (last access: 7 September 2021),
2017.
Eurostat: European Statistical Office database, https://ec.europa.eu/eurostat/data/database (last access: 17 June 2022), 2019a.
Eurostat: European Statistical Office database, Share of irrigable and
irrigated areas in utilised agricultural area (UAA) by NUTS 2 regions, http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=aei_ef_ir&lang=en (last access: 17 June 2022), 2019b.
Eurostat: Annual crop statistics handbook, 2020 Edn.,
https://ec.europa.eu/eurostat/cache/metadata/Annexes/apro_cp_esms_an1.pdf (last access: 17 June 2022), 2020.
Ewert, F., Van Ittersum, M. K., Heckelei, T., Therond, O., Bezlepkina, I., and
Andersen, E.: Scale changes and model linking methods for integrated
assessment of agri-environmental systems, Agr. Ecosyst. Environ., 142,
6–17, https://https://doi.org/10.1016/j.agee.2011.05.016, 2011.
Faivre, R., Leenhardt, D., Voltz, M., Benoit, M., Papy, F., Dedieu, G.,
and Wallach, D.: Spatialising crop models, Agronomie, 24, 205–217,
https://doi.org/10.1051/agro:2004016, 2004.
FAOSTAT (Food and Agriculture Organization of the United Nations): FAOSTAT Database, Rome, Italy, https://www.fao.org/faostat/en/#home, last access: 17 June 2022.
Ferrara, R. M., Carozzi, M., Decuq, C., Loubet, B., Finco, A., Marzuoli, R.,
Gerosa, G., Di Tommasi, P., Magliulo, V., and Rana, G.: Ammonia, nitrous oxide,
carbon dioxide, and water vapor fluxes after green manuring of faba bean
under Mediterranean climate, Agr. Ecosyst. Environ., 315, 107439,
https://doi.org/10.1016/j.agee.2021.107439, 2021.
Fischer, G., Shah, M., Tubiello, F. N., and von Velthuizen, H.:
Socio-economic and climate change impacts on agriculture: An integrated
assessment, 1990–2080, Philos. T. R. Soc. Lond. Ser. B, 360, 2067–2083,
https://doi.org/10.1098/rstb.2005.1744, 2005.
Fitton, N., Alexander, P., Arnell, N., Bajzelj, B., Calvin, K., Doelman, J.,
Gerber, J. S., Havlik, P., Hasegawa, T., Herrero, M., Krisztin, T., van
Meijl, H., Powell, T., Sands, R., Stehfest, E., West, P. C., and Smith, P.:
The vulnerabilities of agricultural land and food production to future water
scarcity, Glob. Environ. Change, 58, 101944, https://doi.org/10.1016/j.gloenvcha.2019.101944, 2019.
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, A., 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/0.1371/journal.pone.0221862, 2019.
Fuss, S., Jones, C. D., Kraxner, F., Peters, G. P., Smith, P., Tavoni, M., Van
Vuuren, D. P., Canadell, J. G., Jackson, R. B., Milne, J., Moreira, J. R.,
Nakicenovic, N., Sharifi, A., and Yamagata, Y.: Research priorities for
negative emissions, Environ. Res. Lett., 11, 115007, https://doi.org/10.1088/1748-9326/11/11/115007, 2016.
Gabrielle, B., Da-Silveira, J., Houot, S., and Michelin, J.: Field-scale
modelling of carbon and nitrogen dynamics in soils amended with urban waste
composts, Agr. Ecosyst. Environ., 110, 289e299, https://doi.org/10.1016/j.agee.2005.04.015, 2005.
Goglio, P., Colnenne-David, C., Laville, P., Doré, T., and Gabrielle,
B.: 29 % N2O emission reduction from a modelled low-greenhouse gas
cropping system during 2009–2011, Environ. Chem. Lett., 11, 143–149,
https://doi.org/doi.org/10.1007/s10311-012-0389-8, 2013.
Gottschalk, P., Wattenbach, M., Neftel, A., Fuhrer, J., Jones, M., Lanigan, G.,
Davis, P., Campbell, C., Soussana, J.-F., and Smith, P.: The role of measurement
uncertainties for the simulation of grassland net ecosystem exchange (NEE)
in Europe, Agr. Ecosyst. Environ., 121, 175–185, https://doi.org/10.1016/j.agee.2006.12.026, 2007.
Haas, E., Carozzi, M., Massad, R. S, Scheer, C., and Butterbach-Bahl, K.:
Testing the performance of CERES-EGC and LandscapeDNDC to simulate effects
of residue management on soil N2O emissions, ResidueGas deliverable report
4.1,
https://projects.au.dk/fileadmin/projects/residuegas/D_reports/ResidueGas_D4.1.pdf (last access: 17 June 2022), 2021.
Haas, E., Carozzi, M., Massad, R. S., Butterbach-Bahl, K., and Scheer, C.:
Long term impact of residue management on soil organic carbon stocks and
nitrous oxide emissions from European croplands, Sci. Total Environ., 154932,
https://doi.org/10.1016/j.scitotenv.2022.154932, 2022.
Haas, E., Klatt, S., Fröhlich, A., Kraft, P., Werner, C., Kiese, R.,
Grote, R., Breuer, L., and Butterbach-Bahl, K.: LandscapeDNDC: A process
model for simulation of biosphere-atmosphere-hydrosphere exchange processes
at site and regional scale, Landscape Ecol., 28, 615–636, https://doi.org/10.1007/s10980-012-9772-x, 2013.
Hansen, J. W. and Jones, J. W.: Scaling-up crop models for climate variability
applications, Agr. Syst., 65, 43–72, https://doi.org/10.1016/S0308-521X(00)00025-1, 2000.
Hassink, J. and Whitmore, A. P.: A Model of the Physical Protection of
Organic Matter in Soils, Soil Sci. Soc. Am. J., 61, 131–139, https://doi.org/10.2136/sssaj1997.03615995006100010020x, 1997.
Hénault, C., Bizouard, F., Laville, P., Gabrielle, B., Nicoullaud, B.,
Germon, J. C., and Cellier, P.: Predicting in situ soil N2O emission using
NOE algorithm and soil database, Glob. Change Biol., 11, 115–127, https://doi.org/10.1111/j.1365-2486.2004.00879.x, 2005.
Hiederer, R.: Mapping Soil Properties for Europe – Spatial Representation of
Soil Database Attributes, Luxembourg, Publications Office of the European
Union – 2013, 47 pp., EUR26082EN Scientific and Technical Research series,
ISSN 1831–9424, https://doi.org/10.2788/94128, 2013.
Hoffmann, H., Zhao, G., Asseng, S., Bindi, M., Biernath, C., Constantin, J.,
Coucheney, E., Dechow, R., Doro, L., Eckersten, H., Gaiser, T., Grosz, B.,
Heinlein, F., Kassie, B., Kersebaum, K., Klein, C., Kuhnert, M., Lewan, E.,
Moriondo, M., Nendel, C., Priesack, E., Raynal, H., Roggero, P., Rötter, R.,
Siebert, S., Specka, X., Tao, F., Teixeira, E., Trombi, G., Wallach, D.,
Weihermüller, L., Yeluripati, J., and Ewert, F.: Impact of spatial soil and
climate input data aggregation on regional yield simulations, PloS One, 11,
e0151782, https://doi.org/10.1371/journal.pone.0151782, 2016.
Hörtnagl, L., Barthel, M., Buchmann, N., Eugster, W., Butterbach-Bahl,
K., Díaz-Pinés, E., Zeeman, M., Klumpp, K., Kiese, R., Bahn, M.,
Hammerle, A., Lu, H., Ladreiter-Knauss, T., Burri, S., and Merbold, L.:
Greenhouse gas fluxes over managed grasslands in Central Europe, Glob.
Change Biol., 24, 1843–1872, https://doi.org/10.1111/gcb.14079, 2018.
Howden, S. M., Soussana, J.-F., Tubiello, F. N., Chhetri, N., Dunlop, M., and
Meinke, H.: Adapting agriculture to climate change, P. Natl. Acad. Sci.
USA, 104, 19691–19696, https://doi.org/10.1073/pnas.0701890104,
2007.
Hsu, J. S., Powell, J., and Adler, P. B.: Sensitivity of mean annual primary
production to precipitation, Glob. Change Biol., 18, 2246–2255,
https://doi.org/10.1111/j.1365-2486.2012.02687.x, 2012.
IPCC: Climate Change 2013: The Physical Science Basis, Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor,
M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley,
P. M., Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 1535, https://doi.org/10.1017/CBO9781107415324,
2013.
IPCC: Global warming of 1.5 ∘C. An IPCC Special Report on the
impacts of global warming of 1.5 ∘C above pre-industrial levels
and related global greenhouse gas emission pathways, in the context of
strengthening the global response to the threat of climate change,
sustainable development, and efforts to eradicate poverty, edited by:
Masson-Delmotte, V., Zhai, P., Pörtner, H. O., Roberts, D., Skea, J.,
Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J.
B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T.,
Tignor, M., and Waterfield, T., Cambridge University Press, https://doi.org/10.1017/9781009157940, 2018.
Jones, C. A. and Kiniry, J. R.: CERES-Maize: A simulation model of maize
growth and development, Texas A&M University Press, Temple, TX, USA, ISBN
08-909-62693, 1986.
Jones, C. D., Hughes, J. K., Bellouin, N., Hardiman, S. C., Jones, G. S.,
Knight, J., Liddicoat, S., O'Connor, F. M., Andres, R. J., Bell, C., Boo,
K.-O., Bozzo, A., Butchart, N., Cadule, P., Corbin, K. D.,
Doutriaux-Boucher, M., Friedlingstein, P., Gornall, J., Gray, L., Halloran,
P. R., Hurtt, G., Ingram, W. J., Lamarque, J.-F., Law, R. M., Meinshausen,
M., Osprey, S., Palin, E. J., Parsons Chini, L., Raddatz, T., Sanderson, M.
G., Sellar, A. A., Schurer, A., Valdes, P., Wood, N., Woodward, S.,
Yoshioka, M., and Zerroukat, M.: The HadGEM2-ES implementation of CMIP5
centennial simulations, Geosci. Model Dev., 4, 543–570, https://doi.org/10.5194/gmd-4-543-2011, 2011.
Jones, S. K., Helfter, C., Anderson, M., Coyle, M., Campbell, C., Famulari,
D., Di Marco, C., van Dijk, N., Tang, Y. S., Topp, C. F. E., Kiese, R.,
Kindler, R., Siemens, J., Schrumpf, M., Kaiser, K., Nemitz, E., Levy, P. E.,
Rees, R. M., Sutton, M. A., and Skiba, U. M.: The nitrogen, carbon and
greenhouse gas budget of a grazed, cut and fertilised temperate grassland,
Biogeosciences, 14, 2069–2088, https://doi.org/10.5194/bg-14-2069-2017, 2016.
Kanter, D., Zhang, X., Mauzerall, D., Malyshev, S., and Shevliakova, E.: The
importance of climate change and nitrogen use efficiency for future nitrous
oxide emissions from agriculture, Environ. Res. Lett., 11, 094003, https://doi.org/10.1088/1748-9326/11/9/094003, 2016.
Kirschbaum, M. U. F.: The temperature dependence of soil organic matter
decomposition, and the effect of global warming on soil organic C storage,
Soil Biol. Biochem., 27, 753–760, https://doi.org/10.1016/0038-0717(94)00242-s, 1995.
Kutsch, W. L., Aubinet, M., Buchmann, N., Smith, P., Osborne, B., Eugster,
W., Wattenbach, M., Schrumpf, M., Schulze, E. D., Tomelleri, E., Ceschia,
E., Bernhofer, C., Beziat, P., Carrara, A., Di Tommasi, P., Grünwald,
T., Jones, M., Magliulo, V., Marloie, O., Moureaux, C., Olioso, A., Sanz, M.
J., Saunders, M., Søgaard, H., and Ziegler, W.: The net biome production
of full crop rotations in Europe, Agr. Ecosyst. Environ., 139, 336–345,
https://doi.org/10.1016/j.agee.2010.07.016, 2010.
Lassaletta, L., Billen, G., Grizzetti, B., Anglade J., and Garnier J.: 50
year trends in nitrogen use efficiency of world cropping systems: The
relationship between yield and nitrogen input to cropland, Environ. Res. Lett., 9,
105011, https://doi.org/10.1088/1748-9326/9/10/105011,
2014.
Lawton, D., Leahy, P., Kiely, G., Byrne, K., and Calanca, P.: Modeling of net
ecosystem exchange and its components for a humid grassland ecosystem,
J. Geophys. Res.-Biogeo., 111, G04013, https://doi.org/10.1029/2006JG000160, 2006.
Lehmann, N., Finger, R., Klein, T., Calanca, P., and Walter, A.: Adapting crop
management practices to climate change: Modeling optimal solutions at the
field scale, Agr. Syst., 117, 55–65, https://doi.org/10.1016/j.agsy.2012.12.011, 2013.
Lehuger, S., Gabrielle, B., Laville, P., Lamboni, M., Loubet, B., and
Cellier, P.: Predicting and mitigating the net greenhouse gas emissions of
crop rotations in Western Europe, Agr. Forest Meteorol., 151,
1654–1671, https://doi.org/10.1016/j.agrformet.2011.07.002,
2011.
Lehuger, S., Gabrielle, B., Oijen, M. Van, Makowski, D., Germon, J., Morvan,
T., Hénault, C., Lehuger, S., Gabrielle, B., Oijen, M. Van, Makowski,
D., and Germon, J.: Bayesian calibration of the nitrous oxide emission
module of an agro-ecosystem model, Agr. Ecosyst. Environ., 133, 208–222,
https://doi.org/10.1016/j.agee.2009.04.022, 2009.
Lehuger, S., Gabrielle, B., Cellier, P., Loubet, B., Roche, R., Béziat,
P., Ceschia, E., and Wattenbach, M.: Predicting the net carbon exchanges of
crop rotations in Europe with an agro-ecosystem model, Agr. Ecosyst.
Environ., 139, 384–395, https://doi.org/10.1016/j.agee.2010.06.011, 2010.
Leip, A., Marchi, G., Koeble, R., Kempen, M., Britz, W., and Li, C.: Linking
an economic model for European agriculture with a mechanistic model to
estimate nitrogen and carbon losses from arable soils in Europe,
Biogeosciences, 5, 73–94, https://doi.org/10.5194/bg-5-73-2008,
2008.
Leip, A., Britz, W., Weiss, F., and de Vries, W.: Farm, land, and soil
nitrogen budgets for agriculture in Europe calculated with CAPRI, Environ.
Pollut., 159, 3243–3253, https://doi.org/10.1016/j.envpol.2011.01.040, 2011.
Lesschen, J. P., van den Berg, M., Westhoek, H. J., Witzke, H. P., and Oenema,
O.: Greenhouse gas emission profiles of European livestock sectors, Anim.
Feed Sci. Technol., 166/167, 16–28, https://doi.org/10.1016/j.anifeedsci.2011.04.058, 2011.
Li, X., Sørensen, P., Olesen, J. E., and Petersen, S. O.: Evidence for
denitrification as main source of N2O emission from residue-amended soil,
Soil Biol. Biochem., 92, 153–160, https://doi.org/10.1016/j.soilbio.2015.10.008, 2016.
Lobell, D. B. and Tebaldi, C.: Getting caught with our plants down: The
risks of a global crop yield slowdown from climate trends in the next two
decades, Environ. Res. Lett., 9, 074003, https://doi.org/10.1088/1748-9326/9/7/074003, 2014.
Lugato, E., Zuliani, M., Alberti, G., Vedove, G. D., Gioli, B., Miglietta,
F., and Peressotti, A.: Application of DNDC biogeochemistry model to
estimate greenhouse gas emissions from Italian agricultural areas at high
spatial resolution, Agr. Ecosyst. Environ., 139, 546–556, https://doi.org/10.1016/j.agee.2010.09.015, 2010.
Lugato, E., Bampa, F., Panagos, P., Montanarella, L., and Jones, A.:
Potential carbon sequestration of European arable soils estimated by
modelling a comprehensive set of management practices, Glob. Change Biol.,
20, 3557–3567, https://doi.org/10.1111/gcb.12551,
2014.
Lugato, E., Paniagua, L., Jones, A., de Vries, W., and Leip, A.: Complementing
the topsoil information of the Land Use/Land Cover Area Frame Survey (LUCAS)
with modelled N2O emissions, PLoS ONE, 12, e0176111, https://doi.org/10.1371/journal.pone.0176111, 2017.
Lugato, E., Leip, A., and Jones, A.: Mitigation potential of soil carbon
management overestimated by neglecting N2O emissions, Nat. Clim. Change, 8,
219–223, https://doi.org/10.1038/s41558-018-0087-z, 2018.
Ma, S., Lardy, R., Graux, A.-I., Ben Touhami, H., Klumpp, K., Martin, R.,
and Bellocchi, G.: Regional-scale analysis of carbon and water cycles on
managed grassland systems, Environ. Modell. Softw., 72, 356–371, https://doi.org/10.1016/j.envsoft.2015.03.007, 2015.
Maaz, T. M., Sapkota, T. B., Eagle, A. J., Kantar, M. B., Bruulsema, T. W.,
and Majumdar, K.: Meta-analysis of yield and nitrous oxide outcomes for
nitrogen management in agriculture, Glob. Change Biol., 27,
2343–2360, https://https://doi.org/10.1111/gcb.15588, 2021.
Martínez-López J., Bagstad K.J., Balbi S., Magrach A., Voigt B.,
Athanasiadis I., Pascual M., Willcock S., and Villa F.: Towards globally
customizable ecosystem service models, Sci. Total Environ., 650,
2325–2336, https://doi.org/10.1016/j.scitotenv.2018.09.371,
2019.
Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter,
R. P., Boote, K. J., Ruane, A. C., Thorburn, P. J., Cammarano, D., and Hatfield,
J. L.: Multimodel ensembles of wheat growth: many models are better than one,
Glob. Change Biol., 21, 911–925, https://doi.org/10.1111/gcb.12768, 2015.
Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Mücher, C. A., and
Watkins, J. W.: A climatic stratification of the environment of Europe,
Glob. Ecol. Biogeogr., 14, 549–563,
https://doi.org/10.1111/j.1466-822X.2005.00190.x, 2005.
Minasny, B., Malone, B. P., McBratney, A. B., Angers, D. A., Arrouays, D.,
Chambers, A., Chaplot, V., Chen, Z.-S., Cheng, K., Das, B. S., Field, D. J.,
Gimona, A., Hedley, C. B., Hong, S. Y., Mandal, B., Marchant, B. P., Martin,
M., McConkey, B. G., Mulder, V. L., O'Rourke, S., Richer-de-Forges, A. C.,
Odeh, I., Padarian, J., Paustian, K., Pan, G., Poggio, L., Savin, I.,
Stolbovoy, V., Stockmann, U., Sulaeman, Y., Tsui, C.-C., Vågen, T.-G.,
van Wesemael, B., and Winowiecki, L.: Soil carbon 4 per mille, Geoderma,
292, 59–86, https://doi.org/10.1016/j.geoderma.2017.01.002,
2017.
Minoli, S., Müller, C., Elliott, J., Ruane, A.C., Jägermeyr, J.,
Zabel, F., Dury, M., Folberth, C., François, L., Hank, T. B., Jacquemin,
I., Liu, W., Olin, S., and Pugh, T. A.: Global response patterns of major
rainfed crops to adaptation by maintaining current growing periods and
irrigation, Earth's Future, 7, 1464–80, https://doi.org/10.1029/2018EF001130, 2019.
Molina, J. A. E., Clapp, C. E., Shaffer, M. J., Chichester, F. W., and Larson,
W. E.: NCSOIL, a model of nitrogen and carbon transformations in soil:
description, calibration, and behavior, Soil Sci. Soc. Am. J., 47, 85–91,
https://doi.org/10.2136/sssaj1983.03615995004700010017x,
1983.
Molina-Herrera, S., Haas, E., Klatt, S., Kraus, D., Augustin, J., Magliulo,
V., Tallec, T., Ceschia, E., Ammann, C., Loubet, B., Skiba, U., Jones, S.,
Brümmer, C., Butterbach-Bahl, K., and Kiese, R.: A modeling study on
mitigation of N2O emissions and NO3 leaching at different agricultural sites
across Europe using LandscapeDNDC, Sci. Total Environ., 553, 128–140,
https://doi.org/10.1016/j.scitotenv.2015.12.099, 2016.
Morais, T. G., Teixeira, R. F., and Domingos, T.: Detailed global modelling of
soil organic carbon in cropland, grassland and forest soils, PLoS ONE, 14,
e0222604, https://doi.org/10.1371/journal.pone.0222604, 2019.
Mueller, B., Hauser, M., Iles, C., Rimi, R. H., Zwiers, F. W., and Wan, H.:
Lengthening of the growing season in wheat and maize producing regions,
Weather Clim. Extrem., 9, 47–56, https://doi.org/10.1016/j.wace.2015.04.001, 2015.
Myrgiotis, V., Williams, M., Rees, R. M., and Topp, C. F. E.: Estimating the
soil N2O emission intensity of croplands in northwest Europe,
Biogeosciences, 16, 1641–1655, https://doi.org/10.5194/bg-16-1641-2019, 2019.
Nicolardot, B., Molina, J. A. E., and Allard, M. R.: C and N fluxes between
pools of soil organic matter: model calibration with long-term incubation
data, Soil Biol. Biochem., 26, 235–243, https://doi.org/10.1016/0038-0717(94)90163-5, 1994.
Niu, X., Easterling, W., Hays, C. J., Jacobs, A., and Mearns, L.: Reliability and
input-data induced uncertainty of EPIC model to estimate climate change
impact on sorghum yields in the U.S, Great Plains, Agr. Ecosyst. Environ.,
129, 268–276, https://doi.org/10.1016/j.agee.2008.09.012, 2009.
Olesen, J. E.: Climate change impacts on society: Section 5.3, Agriculture, in:
Climate change, impacts and vulnerability in Europe 2016: An indicator-based
report, European Environment Agency, 1, 223–244, 2017.
Olesen, J. E. and Bindi, M.: Consequences of climate change for European
agricultural productivity, land use and policy, Eur. J. Agron., 16, 239–262,
https://doi.org/10.1016/S1161-0301(02)00004-7, 2002.
Olesen, J. E., Trnka, M., Kersebaum, K. C., Skjelvåg, A. O., Seguin, B.,
Peltonen-Sainio, P., Rossi, F., Kozyra, J., and Micale, F.: Impacts and
adaptation of European crop production systems to climate change, Eur. J.
Agron., 34, 96–112, https://doi.org/10.1016/j.eja.2010.11.003,
2011.
Olesen, J. E., Børgesen, C. D., Elsgaard, L., Palosuo, T., Rötter, R. P.,
Skjelvåg, A. O., Peltonen-Sainio, P., Börjesson, T., Trnka, M., Ewert, F.,
Siebert, S., Brisson, N., Eitzinger, J., van Asselt, E. D., Oberforster, M., and
van der Fels-Klerx, H. J.: Changes in time of sowing, flowering and maturity
of cereals in Europe under climate change, Food Addit. Conta.m Pt. A, 29,
1527–1542, https://doi.org/10.1080/19440049.2012.712060, 2012.
Parry, M., Rosenzweig, C., and Livermore, M.: Climate change, global food
supply and risk of hunger, Philos. T. R. Soc. B, 360, 2125–2138,
https://doi.org/10.1098/rstb.2005.1751, 2005.
Parton, W. J., Schimel, D. S., Ojima, D. S., and Cole, C. V.: A general model
for soil organic matter dynamics: sensitivity to litter chemistry, texture
and management, in:
Quantitative modeling of soil farming processes, edited by: Bryant, R. B. and Arnold, R. W., SSSA Special Publication
39, ASA, CSSA, and SSA, Madison, Wisconsin, USA, 147–167, https://doi.org/10.2136/sssaspecpub39.c9, 1994.
Ramirez-Villegas, J., Watson, J., and Challinor, A. J.: Identifying traits for
genotypic adaptation using crop models, J. Exp. Bot., 66, 3451–3462,
https://doi.org/10.1093/jxb/erv014, 2015.
Reay, D. S., Davidson, E. A., Smith, K. A., Smith, P., Melillo, J. M., Dentener,
F., and Crutzen, P. J.: Global agriculture and nitrous oxide emissions, Nat.
Clim. Change, 2, 410–416, https://doi.org/10.1038/NCLIMATE1458, 2012.
Reinds, G. J., Heuvelink, G. B. M., Hoogland, T., Kros, J., and de Vries, W.:
Estimating nitrogen fluxes at the European scale by upscaling INTEGRATOR
model outputs from selected sites, Biogeosciences, 9, 4527–4536, https://doi.org/10.5194/bg-9-4527-2012, 2012.
Reuter, H. I, Rodriguez Lado, L., Hengl, T., and Montanarella, L.:
Continental Scale Digital Soil Mapping using European Soil Profile Data:
soil pH, in: SAGA –
Seconds Out, edited by: Böhner, J., Blaschke, T., and Montanarella, L., Hamburger Beiträge zur Physischen Geographie und
Landschaftsökologie, Heft 19, Universität Hamburg, Institut für
Geographie, https://esdac.jrc.ec.europa.eu/public_path/shared_folder/dataset/10_soil_ph/soil_ph_in_europe_hbpl19_10.pdf (last access: 17 June 2022), 2008.
Riedo, M., Grub, A., Rosset, M., and Fuhrer, J. A.: Pasture Simulation model for
dry matter production, and fluxes of carbon, nitrogen, water and energy,
Ecol. Model., 105, 141–183, https://doi.org/10.1016/S0304-3800(97)00110-5, 1998.
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, 166–172,
https://doi.org/10.1016/j.agrformet.2012.09.011, 2013.
Rumpel, C., Amiraslani, F., Chenu, C., Garcia Cardenas, M., Kaonga, M.,
Koutika, L. S., Ladha, J., Madari, B., Shirato, Y., Smith, P., Soudi, B.,
Soussana, J.-F., Whitehead, D., and Wollenberg, E.: The 4p1000 initiative:
Opportunities, limitations and challenges for implementing soil organic
carbon sequestration as a sustainable development strategy, Ambio, 1–11,
https://doi.org/10.1007/s13280-019-01165-2, 2019.
Sacks, W. J., Deryng, D., Foley, J. A., and Ramankutty, N.: Crop planting dates: An
analysis of global patterns, Glob. Ecol. Biogeogr., 19, 607–620, https://doi.org/10.1111/j.1466-8238.2010.00551.x, 2010.
Sándor, R., Barcza, Z., Hidy, D., Lellei-Kovács, E., Ma, S., and Bellocchi,
G.: Modelling of grassland fluxes in Europe: Evaluation of two
biogeochemical models, Agr. Ecosyst. Environ., 215, 1–19, https://doi.org/10.1016/j.agee.2015.09.001, 2016.
Sándor, R., Ehrhardt, F., Brilli, L., Carozzi, M., Recous, S., Smith, P., Snow,
V., Soussana, J.-F., Dorich, C. D., Fuchs, K., Fitton, N., Gongadze, K., Klumpp,
K., Liebig, M., Martin, R., Merbold, L., Newton, P. C. D., Rees, R. M., Rolinski, S.,
and Bellocchi, G.: The use of biogeochemical models to evaluate mitigation of
greenhouse gas emissions from managed grasslands, Sci. Total Environ.,
642, 292–306, https://doi.org/10.1016/j.scitotenv.2018.06.020, 2018.
Sansoulet, J., Pattey, E., Kröbel, R., Grant, B., Smith, W., Jégo,
G., Desjardins, R. L., Tremblay, N., and Tremblay, G.: Comparing the
performance of the STICS, DNDC, and DayCent models for predicting N uptake
and biomass of spring wheat in Eastern Canada, Field Crops Res., 156,
135–150, https://doi.org/10.1016/j.fcr.2013.11.010, 2014.
Scarlat, N., Fahl, F., Lugato, E., Monforti, F., and Dallemand, J.:
Integrated and spatially explicit assessment of sustainable crop residues
potential in Europe, Biomass Bioenerg., 122, 257–269, https://doi.org/10.1016/j.biombioe.2019.01.021, 2019.
Schmid, M., Neftel, A., Riedo, M., and Fuhrer, J.: Process-based modelling
of nitrous oxide emissions from different nitrogen sources in mown
grassland, Nutr. Cycl. Agroecosyst., 60, 177–187, https://doi.org/10.1023/A:1012694218748, 2001.
Shcherbak, I., Millar, N., and Robertson, G.P.: Global metaanalysis of the
nonlinear response of soil nitrous oxide (N2O) emissions to fertilizer
nitrogen, P. Natl. Acad. Sci. USA, 111, 9199–9204, https://doi.org/10.1073/pnas.1322434111, 2014.
Smit, H. J., Metzger, M. J., and Ewert, F.: Spatial distribution of grassland
productivity and land use in Europe, Agr. Syst., 98, 208–219,
https://doi.org/10.1016/j.agsy.2008.07.004, 2008.
Smith, P.: Agricultural greenhouse gas mitigation potential globally, in:
Europe and in the UK: what have we learnt in the last 20 years?, Glob.
Change Biol., 18, 35–43, https://doi.org/10.1111/j.1365-2486.2011.02517.x, 2012.
Smith, P.: Soil carbon sequestration and biochar as negative emission
technologies, Glob. Change Biol., 22, 1315–1324, https://doi.org/10.1111/gcb.13178, 2016.
Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H. H., Kumar, P., McCarl,
B., Ogle, S., O'Mara, F., Rice, C., Scholes, R. J., Sirotenko, O., Howden,
M., McAllister, T., Pan, G., Romanenkov, V., Schneider, U., Towprayoon, S.,
Wattenbach, M., and Smith, J. U.: Greenhouse gas mitigation in agriculture,
Philos. T. R. Soc. Lond. Ser. B, 27, 89–813, https://doi.org/10.1098/rstb.2007.2184, 2008.
Smith, P., Heberl, H., Popp, A., Erb, K.-H., Lauk, C., Harper, R., Tubiello,
F., De Pinto, A., Jafari, M., Sohi, S., Masera, O., Bottcher, H., Berndes,
G., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E., Mbow,
C., Ravindranath, N., Rice, C., Abad, C., Romanovskaya, A., Sperling, F.,
Herrero, M., House, J., and Rose, S.: How much land based greenhouse gas
mitigation can be achieved without compromising food security and
environmental goals?, Glob. Change Biol., 19, 2285–2302,
https://doi.org/10.1111/gcb.12160, 2013.
Soussana, J.-F., Tallec, T., and Blanfort, V.: Mitigating the greenhouse gas balance
of ruminant production systems through carbon sequestration in grasslands,
Animal, 4, 334–350, https://doi.org/10.1017/S1751731109990784,
2010.
Soussana, J. F., Allard, V., Pilegaard, K., Ambus, C., Campbell, C., Ceschia,
E., Clifton-Brown, J., Czobel, S., Domingues, R., Flechard, C., Fuhrer, J.,
Hensen, A., Horvath, L., Jones, M., Kasper, G., Martin, C., Nagy, Z.,
Neftel, A., Raschi, A., Baronti, S., Rees, R.M., Skiba, U., Stefani, P.,
Manca, G., Sutton, M., Tuba, Z., and Valentini, R.: Full accounting of the
greenhouse gas (CO2, N2O, CH4) budget of nine European grassland sites, Agr.
Ecosyst. Environ., 121, 121–134, https://doi.org/10.1016/j.agee.2006.12.022, 2007.
Stagge, J. H., Kingston, D. G., Tallaksen, L. M., and Hannah, D. M.: Observed
drought indices show increasing divergence across Europe. Sci. Rep., 7,
14045. https://doi.org/10.1038/s41598-017-14283-2, 2017.
Steduto, P., Hsiao, T. C., Fereres, E., and Raes, D.: Crop Yield
Response to Water, Irrigation and Drainage Paper 66, United Nations FAO,
Rome, ISBN 978-92-5-107274-5, 2012.
Stehfest, E. and Bouwman, L.: N2O and NO emission from agricultural fields
and soils under natural vegetation: summarizing available measurement data
and modeling of global annual emissions, Nutr. Cycl. Agroecosyst., 74,
207–228, https://doi.org/10.1007/s10705-006-9000-7, 2006.
Stella, T., Mouratiadou, I., Gaiser, T., Berg-Mohnicke, M., Wallor, E.,
Ewert, F., and Nendel, C.: Estimating the contribution of crop residues to
soil organic carbon conservation, Environ. Res. Lett., 14, 094008, https://doi.org/10.1088/1748-9326/ab395c, 2019.
Sutton, M., Billen, G., Bleeker, A., Erisman, J. W., Grennfelt, P.,
Grinsven, H., Grizzetti, B., Howard, C., and Leip, A.: The European Nitrogen Assessment: Sources, Effects and Policy Perspectives, Cambridge University Press, https://doi.org/10.1017/CBO9780511976988, 2011.
Tao, F. and Zhang, Z.: Impacts of climate change as a function of global
mean temperature: maize productivity and water use in China, Climatic Change,
105, 409–432, https://doi.org/10.1007/s10584-010-9883-9, 2011.
Therond, O., Hengsdijk, H., Casellas, E., Wallach, D., Adam, M.,
Belhouchette, H., Oomen, R., Russell, G., Ewert, F., Bergez, J. E., and
Janssen, S.: Using a cropping system model at regional scale: Low-data
approaches for crop management information and model calibration, Agr.
Ecosyst. Environ., 142, 85–94, https://doi.org/10.1016/j.agee.2010.05.007, 2011.
Tian, H., Xu, R., Canadell, J. G., Thompson, R. L., Winiwarter, W., Suntharalingam, P., Davidson, E. A., Ciais, P., Jackson, R. B., Janssens-Maenhout, G., Prather, M. J., Regnier, P., Pan, N., Pan, S., Peters, G. P., Shi, H., Tubiello, F. N., Zaehle, S., Zhou, F., Arneth, A., Battaglia, G., Berthet, S., Bopp, L., Bouwman, A. F., Buitenhuis, E. T., Chang, J., Chipperfield, M. P., Dangal, S. R. S., Dlugokencky, E., Elkins, J. W., Eyre, B. D., Fu, B., Hall, B., Ito, A., Joos, F., Krummel, P. B., Landolfi, A., Laruelle, G. G., Lauerwald, R., Li, W., Lienert, S., Maavara, T., MacLeod, M., Millet, D. B., Olin, S., Patra, P. K., Prinn, R. G., Raymond, P. A., Ruiz, D. J., van der Werf, G. R., Vuichard, N., Wang, J., Weiss, R. F., Wells, K. C., Wilson, C., Yang, J., and Yao, Y.: A comprehensive quantification of global nitrous oxide sources and sinks, Nature, 586, 248–256, https://doi.org/10.1038/s41586-020-2780-0, 2020.
Tubiello, F. N., Soussana, J. F., Howden, M., and Easterling, W.: Crop and
pasture responses to climate change: fundamental processes, P. Natl.
Acad. Sci. USA., 104, 19686–19690, https://doi.org/10.1073/pnas.0701728104, 2007.
Tubiello, F. N. and Rosenzweig, C.: Developing climate change impact metrics
for agriculture, Integrat. Assess. J., 8, 165–184, 2008.
Turral, H., Burke, J., and Faurès, J.-M.: Climate change, water and food security, FAO Water Reports, 36, FAO, Rome, ISBN 978-9-25106-7-956, 2011.
UNEP: Drawing Down N2O to Protect Climate and the Ozone Layer. A UNEP Synthesis Report, United Nations Environment Programme (UNEP), Nairobi, Kenya, ISBN 978-92-807-3358-7, 2013.
USDA: Major World Crop Areas and Climatic Profiles. World Agricultural Outlook Board, U.S. Department of Agriculture, Agricultural Handbook No. 664, https://naldc.nal.usda.gov/download/CAT88895275/PDF (last access: 17 June 2022), 1994.
van der Velde, M., Bouraoui, F., and Aloe, A.: Pan-European regional-scale
modelling ofwater and N efficiencies of rapeseed cultivation for biodiesel
production, Glob. Change Biol., 15, 24–37, https://doi.org/10.1111/j.1365-2486.2008.01706.x, 2009.
Van Oijen, M., Balkovi, J., Beer, C., Cameron, D.R., Ciais, P., Cramer, W.,
Kato, T., Kuhnert, M., Martin, R., Myneni, R., and Rammig, A.: Impact of
droughts on the carbon cycle in European vegetation: a probabilistic risk
analysis using six vegetation models, Biogeosciences, 11, 6357–6375,
https://doi.org/10.5194/bg-11-6357-2014, 2014.
Voglmeier, K., Six, J., Jocher, M., and Ammann, C.: Grazing-related nitrous
oxide emissions: From patch scale to field scale, Biogeosciences, 16,
1685–1703, https://doi.org/10.5194/bg-16-1685-2019, 2019.
Vuichard, N., Ciais, P., Viovy, N., Calanca, P., and Soussana, J.-F.:
Estimating the greenhouse gas fluxes of European grasslands with a
process-based model: 2. Simulations at the continental level, Global
Biogeochem. Cy., 21, GB1005, https://doi.org/10.1029/2005gb002612,
2007.
Wada, Y., Wisser, D., Eisner, S., Flörke, M., Gerten, D., Haddeland, I.,
Hanasaki, N., Masaki, Y., Portmann, F. T., Stacke, T., Tessler, Z., and
Schewe, J.: Multimodel projections and uncertainties of irrigation water
demand under climate change, Geophys. Res. Lett., 40, 4626–4632, https://doi.org/10.1002/grl.50686, 2013.
Waha, K., Dietrich, J. P., Portmann, F. T., Siebert, S., Thornton, P. K.,
Bondeau, A., and Herrero, M.: Multiple cropping systems of the world and the
potential for increasing cropping intensity, Glob. Environ. Change, 64,
102131, https://doi.org/10.1016/j.gloenvcha.2020.102131, 2020.
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and
Schewe, J.: The Inter-Sectoral Impact Model Intercomparison Project
(ISI-MIP): project framework, P. Natl. Acad. Sci. USA, 111, 3228–3232,
https://doi.org/10.1073/pnas.1312330110, 2014.
Wattenbach, M., Sus, O., Vuichard, N., Lehuger, S., Gottschalk, P., Li, L.,
Leip, A., Williams, M., Tomelleri, E., Kutsch, W. L., Buchmann, N., Eugster,
W., Dietiker, D., Aubinet, M., Ceschia, E., Béziat, P., Grünwald,
T., Hastings, A., Osborne, B., Ciais, P., Celier, P., and Smith, P.: The
carbon balance of European croplands: A cross-site comparison of simulation
models, Agr. Ecosyst. Environ., 139, 419–453, https://doi.org/10.1016/j.agee.2010.08.004, 2010.
Wattenbach, M., Lüdtke, S., Redweik, R., Van Oijen, M., Balkovic, J.,
and Reinds, G.: A generic probability based model to derive regional patterns of
crops in time and space, Geophys. Res. Abstr.,
EGU2015–13153, EGU General Assembly 2015, Vienna, Austria, 2015.
Weitz, A. M., Linder, E., Frolking, S., Crill, P. M., and Keller, M.: N2O
emissions from humid tropical agricultural soils: Effects of soil moisture,
texture and nitrogen availability, Soil Biol. Biochem., 33, 1077–1093, https://doi.org/10.1016/S0038-0717(01)00013-X, 2001.
Wells, K. C., Millet, D. B., Bousserez, N., Henze, D. K., Griffis, T. J.,
Chaliyakunnel, S., Dlugokencky, E. J., Saikawa, E., Xiang, G., Prinn, R. G.,
O'Doherty, S., Young, D., Weiss, R. F., Dutton, G. S., Elkins, J. W.,
Krummel, P. B., Langenfelds, R., and Steele, L. P.: Top-down constraints on
global N2O emissions at optimal resolution: application of a new dimension
reduction technique, Atmos. Chem. Phys., 18, 735–756, https://doi.org/10.5194/acp-18-735-2018, 2018.
Wilkinson, J. M. and Lee, M. R. F.: Use of human-edible animal feeds by ruminant
livestock, Animal, 12, 1–9, https://doi.org/10.1017/S175173111700218X, 2017.
Wint, G. R. W. and Robinson, T. P.: Gridded livestock of the world 2007, Food and
Agriculture Organization of the United Nations (FAO), Rome, Italy, 131 pp. ISBN 978-9-2510-579,
2007.
World Bank: World Development Indicators: Trends in greenhouse gas
emissions, http://wdi.worldbank.org/table/3.9, last access: 7
September 2021.
Yang, C., Fraga, H., van Ieperen, W., Trindade, H., and Santos, J. A.: Effects of
climate change and adaptation options on winter wheat yield under rainfed
Mediterranean conditions in southern Portugal, Climatic Change, 154,
159–178, https://doi.org/10.1007/s10584-019-02419-4, 2019.
Yang, X., Chen, F., Lin, X., Liu, Z., Zhang, H., Zhao, J., Li, K., Ye, Q., Li, Y.,
Lv, S., and Yang, P.: Potential benefits of climate change for crop
productivity in China, Agr. Forest Meteorol., 208, 76–84, https://doi.org/10.1016/j.agrformet.2015.04.024, 2015.
Zhang, Q., Zhang, W., Li, T., Sun, W., Yu, Y., and Wang, G.: Projective
analysis of staple food crop productivity in adaptation to future climate
change in China, Int. J. Biometeorol., 61, 1445–1460, https://doi.org/10.1007/s00484-017-1322-4, 2017.
Zhao, C., Liu, B., Piao, S. L., Wang, X. H., Lobell, D. B., Huang, Y., Huang, M. T.,
Yao, Y. T., Bassu, S., Ciais, P., Durand, J. L., Elliott, J., Ewert, F., Janssens,
I. A., Li, T., Lin, E., Liu, Q., Martre, P., Muller, C., Peng, S. S., Penuelas, J.,
Ruane, A. C., Wallach, D., Wang, T., Wu, D. H., Liu, Z., Zhu, Y., Zhu, Z. C., and
Asseng, S.: Temperature increase reduces global yields of major crops in four
independent estimates, P. Natl. Acad. Sci. USA, 114, 9326–9331,
https://doi.org/10.1073/pnas.1701762114, 2017.
Short summary
Crop and grassland production indicates a strong reduction due to the shortening of the length of the growing cycle associated with rising temperatures. Greenhouse gas emissions will increase exponentially over the century, often exceeding the CO2 accumulation of agro-ecosystems. Water demand will double in the next few decades, whereas the benefits in terms of yield will not fill the gap of C losses due to climate perturbation. Climate change will have a regionally distributed effect in the EU.
Crop and grassland production indicates a strong reduction due to the shortening of the length...
Altmetrics
Final-revised paper
Preprint