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 article
| 
02 Apr 2020
Data description article |
| 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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Latest update: 11 Mar 2026
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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