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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 05 Aug 2019

Submitted as: data description paper | 05 Aug 2019

Review status
A revised version of this preprint is currently under review for the journal ESSD.

Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale

Wei Li1,2, Philippe Ciais2, Elke Stehfest3, Detlef van Vuuren3, Alexander Popp4, Almut Arneth5, Fulvio Di Fulvio6, Jonathan Doelman3, Florian Humpenöder4, Anna Harper7,12, Taejin Park8, David Makowski9,10, Petr Havlik6, Michael Obersteiner6, Jingmeng Wang1, Andreas Krause5,11, and Wenfeng Liu2 Wei Li et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing,100084, China
  • 2Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
  • 3Department of Climate, Air and Energy, Netherlands Environmental Assessment Agency (PBL), The Hague, The Netherlands
  • 4Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
  • 5Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
  • 6International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria
  • 7College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
  • 8Department of Earth and Environment, Boston University, Boston, MA 02215, USA
  • 9CIRED, CIRAD, 45bis Avenue de la Belle Gabrielle, 94130 Nogent-sur-Marne, France
  • 10UMR Agronomie, INRA, AgroParisTech, Université Paris-Saclay, ThivervalGrignon 78850, France
  • 11TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
  • 12College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4QF, UK

Abstract. Most scenarios from Integrated Assessment Models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO2 emissions or even to achieving negative emissions (together with CCS). The potential amount of CO2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random forest algorithm is used to upscale a bioenergy yield dataset of 3,963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosic bioenergy crops under current technology, climate and atmospheric CO2 conditions at a 0.5° × 0.5° spatial resolution. We also provide a combined “best bioenergy crop” yield map by selecting the one of the five crop types with the highest yield in each of the grid cell, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 t DM ha-1 yr-1. High yields mainly occur in the Amazon region and Southeast Asia. We further compare our empirically derived maps with yield maps used in three IAMs and find that the median yields in our maps are > 50 % higher than those in the IAM maps. Our estimates of gridded bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models, or to identify the most suitable lands for future bioenergy crop plantations. The 0.5° × 0.5° global maps for yields of different bioenergy crops and the best crop and for the best crop composition generated from this study can be download from (Li, 2019).

Wei Li et al.

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Wei Li et al.

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Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale W. Li

Wei Li et al.


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