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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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.