Articles | Volume 12, issue 2
https://doi.org/10.5194/essd-12-789-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-789-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
02 Apr 2020
Data description paper |
| 02 Apr 2020
| 02 Apr 2020
Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, 100084,
China
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Elke Stehfest
PBL Netherlands Environmental Assessment Agency, The Hague, the Netherlands
Detlef van Vuuren
PBL Netherlands Environmental Assessment Agency, The Hague, the Netherlands
Alexander Popp
Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
Almut Arneth
Karlsruhe Institute of Technology, Institute of Meteorology and
Climate Research – Atmospheric Environmental Research (IMK-IFU),
Garmisch-Partenkirchen, Germany
Fulvio Di Fulvio
International Institute for Applied Systems Analysis, Ecosystem
Services and Management Program, Schlossplatz 1, 2361, Laxenburg, Austria
Jonathan Doelman
PBL Netherlands Environmental Assessment Agency, The Hague, the Netherlands
Florian Humpenöder
Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
Anna B. Harper
College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
College of Life and Environmental Sciences,
University of Exeter, Exeter EX4 4QF, UK
Taejin Park
Department of Earth and Environment, Boston University, Boston, MA
02215, USA
NASA Ames Research Center, Moffett Field, CA 94035, USA
Bay Area Environmental Research Institute, Moffett Field, CA 94035, USA
David Makowski
CIRED, CIRAD, 45 bis Avenue de la Belle Gabrielle, 94130
Nogent-sur-Marne, France
UMR Agronomie, INRA, AgroParisTech, Université Paris-Saclay,
Thiverval-Grignon 78850, France
Petr Havlik
International Institute for Applied Systems Analysis, Ecosystem
Services and Management Program, Schlossplatz 1, 2361, Laxenburg, Austria
Michael Obersteiner
International Institute for Applied Systems Analysis, Ecosystem
Services and Management Program, Schlossplatz 1, 2361, Laxenburg, Austria
Jingmeng Wang
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, 100084,
China
Andreas Krause
Karlsruhe Institute of Technology, Institute of Meteorology and
Climate Research – Atmospheric Environmental Research (IMK-IFU),
Garmisch-Partenkirchen, Germany
TUM School of Life Sciences Weihenstephan, Technical University of
Munich, Freising, Germany
Wenfeng Liu
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
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31 citations as recorded by crossref.
- Potential of land-based climate change mitigation strategies on abandoned cropland M. Gvein et al. 10.1038/s43247-023-00696-7
- Sustainable one-pot synthesis of novel soluble cellulose-based nonionic biopolymers for natural antimicrobial materials X. Dang et al. 10.1016/j.cej.2023.143810
- Simulating second-generation herbaceous bioenergy crop yield using the global hydrological model H08 (v.bio1) Z. Ai et al. 10.5194/gmd-13-6077-2020
- Energy potentials, negative emissions, and spatially explicit environmental impacts of perennial grasses on abandoned cropland in Europe C. Iordan et al. 10.1016/j.eiar.2022.106942
- Over-reliance on land for carbon dioxide removal in net-zero climate pledges K. Dooley et al. 10.1038/s41467-024-53466-0
- Challenges and opportunities in Machine learning for bioenergy crop yield Prediction: A review J. Lepnaan Dayil et al. 10.1016/j.seta.2024.104057
- Transfer-Learning-Based Approach for Yield Prediction of Winter Wheat from Planet Data and SAFY Model Y. Zhao et al. 10.3390/rs14215474
- Quantifying the impacts of land cover change on gross primary productivity globally A. Krause et al. 10.1038/s41598-022-23120-0
- Global assessment of nature’s contributions to people Y. Liu et al. 10.1016/j.scib.2023.01.027
- Production Cost of Biocarbon and Biocomposite, and Their Prospects in Sustainable Biobased Industries P. Roy et al. 10.3390/su16135633
- Mycorrhizal types determined the response of yield of woody bioenergy crops to environmental factors M. Luo et al. 10.1007/s10123-024-00601-y
- Temperature Changes Induced by Biogeochemical and Biophysical Effects of Bioenergy Crop Cultivation J. Wang et al. 10.1021/acs.est.2c05253
- Bioenergy Crops for Low Warming Targets Require Half of the Present Agricultural Fertilizer Use W. Li et al. 10.1021/acs.est.1c02238
- Alternative Pathway to Phase Down Coal Power and Achieve Negative Emission in China R. Wang et al. 10.1021/acs.est.2c06004
- A protein transition can free up land to tap vast energy and negative emission potentials O. Rueda et al. 10.1016/j.oneear.2023.12.016
- Considerable energy crop production potentials in the Russian Far East Z. Zhang et al. 10.1016/j.biombioe.2024.107365
- Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield S. Chen et al. 10.3390/rs14102340
- Macro-and/or microplastics as an emerging threat effect crop growth and soil health H. Gao et al. 10.1016/j.resconrec.2022.106549
- Global soil organic carbon changes and economic revenues with biochar application M. Han et al. 10.1111/gcbb.12915
- Physiological Response of Miscanthus sinensis (Anderss.) to Biostimulants M. Jańczak-Pieniążek et al. 10.3390/agriculture14010033
- Eco-Friendly Cellulose-Based Nonionic Antimicrobial Polymers with Excellent Biocompatibility, Nonleachability, and Polymer Miscibility X. Dang et al. 10.1021/acsami.3c10902
- A high spatial resolution dataset of China’s biomass resource potential R. Wang et al. 10.1038/s41597-023-02227-7
- Enhanced food system efficiency is the key to China’s 2060 carbon neutrality target M. Ren et al. 10.1038/s43016-023-00790-1
- Land-neutral negative emissions through biochar-based fertilization—assessing global potentials under varied management and pyrolysis conditions C. Werner et al. 10.1007/s11027-024-10130-8
- Energy potentials and water requirements from perennial grasses on abandoned land in the former Soviet Union J. Næss et al. 10.1088/1748-9326/ac5e67
- Bioenergy for climate change mitigation: Scale and sustainability K. Calvin et al. 10.1111/gcbb.12863
- Biochar technology cannot offset land carbon emissions in Guangdong province, China F. Wang et al. 10.1007/s44246-024-00140-1
- A review of influencing factors for policy interventions in the deployment of bioenergy with carbon capture and storage X. Xing et al. 10.1016/j.nxsust.2024.100040
- Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China X. Xing et al. 10.1038/s41467-021-23282-x
- Comparing the climate change mitigation potentials of alternative land uses: Crops for biofuels or biochar vs. natural regrowth A. Løvenskiold et al. 10.1016/j.geosus.2022.11.004
- Global cooling induced by biophysical effects of bioenergy crop cultivation J. Wang et al. 10.1038/s41467-021-27520-0
Latest update: 14 Dec 2024
Short summary
We generated spatially explicit bioenergy crop yields based on field measurements with climate, soil condition and remote-sensing variables as explanatory variables and the machine-learning method. We further compared our yield maps with the maps from three integrated assessment models (IAMs; IMAGE, MAgPIE and GLOBIOM) and found that the median yields in our maps are > 50 % higher than those in the IAM maps.
We generated spatially explicit bioenergy crop yields based on field measurements with climate,...
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