摘要：Most scenarios from integrated assessment models (IAMs) that project greenhouse gas emissionsinclude the use of bioenergy as a means to reduce CO 2 emissions or even to achieve negative emissions (togetherwith CCS – carbon capture and storage). The potential amount of CO 2 that can be removed from the atmospheredepends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiencywith which CO 2 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 ofobservations are currently lacking. In this study, a random-forest (RF) algorithm is used to upscale a bioenergyyield dataset of 3963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climaticand soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosicbioenergy crops under current technology, climate and atmospheric CO 2 conditions at a 0.5 ◦ ×0.5 ◦ spatial res-olution. We also provide a combined “best bioenergy crop” yield map by selecting one of the five crop typeswith the highest yield in each of the grid cells, eucalypt and Miscanthus in most cases. The global median yieldof the best crop is 16.3tDMha −1 yr −1 (DM – dry matter). High yields mainly occur in the Amazon region andsoutheastern Asia. We further compare our empirically derived maps with yield maps used in three IAMs andfind 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 toidentify the most suitable lands for future bioenergy crop plantations. The 0.5 ◦ ×0.5 ◦ global maps for yields ofdifferent bioenergy crops and the best crop and for the best crop composition generated from this study can bedownload from https://doi.org/10.5281/zenodo.3274254 (Li, 2019).
