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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration.
Agric. For. Meteorol. 214-215, 483-493 (2015)
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
3.762
1.888
22
27
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Climate Change ; Crop Model ; Emulator ; Meta-model ; Statistical Model ; Yield
Sprache
englisch
Veröffentlichungsjahr
2015
HGF-Berichtsjahr
2015
ISSN (print) / ISBN
0168-1923
e-ISSN
1873-2240
Zeitschrift
Agricultural and Forest Meteorology
Quellenangaben
Band: 214-215,
Seiten: 483-493
Verlag
Elsevier
Verlagsort
Amsterdam [u.a.]
Begutachtungsstatus
Peer reviewed
POF Topic(s)
20405 - Terrestrial Systems – from Observation to Prediction
Forschungsfeld(er)
Environmental Sciences
PSP-Element(e)
G-504400-003
G-504900-004
G-504900-004
WOS ID
WOS:000364730000043
Scopus ID
84942918718
Erfassungsdatum
2015-10-13