Articles | Volume 12, issue 2
https://doi.org/10.5194/essd-12-789-2020
https://doi.org/10.5194/essd-12-789-2020
Data description paper
 | 
02 Apr 2020
Data description paper |  | 02 Apr 2020

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

Wei Li, Philippe Ciais, Elke Stehfest, Detlef van Vuuren, Alexander Popp, Almut Arneth, Fulvio Di Fulvio, Jonathan Doelman, Florian Humpenöder, Anna B. Harper, Taejin Park, David Makowski, Petr Havlik, Michael Obersteiner, Jingmeng Wang, Andreas Krause, and Wenfeng Liu

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Latest update: 28 Mar 2024
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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.
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