Articles | Volume 12, issue 2
Earth Syst. Sci. Data, 12, 789–804, 2020
https://doi.org/10.5194/essd-12-789-2020
Earth Syst. Sci. Data, 12, 789–804, 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 et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Wei Li on behalf of the Authors (19 Jan 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (22 Jan 2020) by Scott Stevens
RR by Anonymous Referee #2 (10 Feb 2020)
ED: Publish subject to technical corrections (25 Feb 2020) by Scott Stevens
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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.