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

Abstract

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 https://doi.org/10.5281/zenodo.3274254 (Li, 2019).