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

Li, W., Ciais, P., Stehfest, E., Vuuren, D. P. v., Popp, A., Arneth, A., Di Fulvio, F., Doelman, J., Humpenöder, F., Harper, A., Park, T., Makowski, D., Havlik, P., Obersteiner, M., Wang, J., Krause, A., Liu, W. (2020): Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale. - Earth System Science Data, 12, 2, 789-804.
https://doi.org/10.5194/essd-2019-118

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 Urheber:
Li, W.1, Autor
Ciais, P.1, Autor
Stehfest, E.1, Autor
Vuuren, D. P. van1, Autor
Popp, Alexander2, Autor              
Arneth, A.1, Autor
Di Fulvio, F.1, Autor
Doelman, J.1, Autor
Humpenöder, Florian2, Autor              
Harper, A.1, Autor
Park, T.1, Autor
Makowski, D.1, Autor
Havlik, P.1, Autor
Obersteiner, M.1, Autor
Wang, J.1, Autor
Krause, A.1, Autor
Liu, W.1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: 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 achieve negative emissions (together with CCS – carbon capture and storage). 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 (RF) algorithm is used to upscale a bioenergy yield dataset of 3963 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 one of the five crop types with the highest yield in each of the grid cells, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 t DM ha−1 yr−1 (DM – dry matter). High yields mainly occur in the Amazon region and southeastern 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).

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 Datum: 2020
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.5194/essd-2019-118
PIKDOMAIN: RD3 - Transformation Pathways
eDoc: 8700
MDB-ID: yes - 3025
Organisational keyword: RD3 - Transformation Pathways
Research topic keyword: Food & Agriculture
Research topic keyword: Land use
Regional keyword: Global
Working Group: Land-Use Management
 Art des Abschluß: -

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Titel: Earth System Science Data
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, oa
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 12 (2) Artikelnummer: - Start- / Endseite: 789 - 804 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals2_126
Publisher: Copernicus