Journal
AGRICULTURAL AND FOREST METEOROLOGY
Volume 214, Issue -, Pages 483-493Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.agrformet.2015.09.013
Keywords
Climate change; Crop model; Emulator; Meta-model; Statistical model; Yield
Categories
Funding
- Victorian Department of Economic Development Jobs, Transport and Resources
- Australian Department of Agriculture
- INRA ACCAF meta-program
- Ministry of Science, Research and Arts of Baden-Wurttemberg [AZ Zu33-721.3-2]
- Helmholtz Centre for Environmental Research - UFZ, Leipzig
- FACCE MACSUR project by Innovation Fund Denmark
- FACCE MACSUR project through the German Federal Ministry of Education and Research [031A103B, 2812ERA115]
- Helmholtz project 'REKLIMRegional Climate Change: Causes and Effects' Topic 5: 'Chemistry-climate interactions on global to regional scales'
- Royal Society of New Zealand
- Climate Change Impacts and Implications for New Zealand (CCII) project
- Texas AgriLife Research and Extension, Texas AM University
- USDA National Institute for Food and Agriculture [32011-680002-30191]
- FACCE MACSUR project by the German Ministry for Education and Research (BMBF) [031A103B]
- FACE MACSUR project through the German Federal Office for Agriculture and Food [2812ERA147]
- BMBF via the CARBIOCIAL research project [01LL0902M]
- National High-Tech Research and Development Program of China [2013AA100404]
- Priority Academic Program Development of Jiangsu Higher Education Institutions in China (PAPD)
- BBSRC [BBS/E/C/00005205, BB/K00882X/1, BB/N004825/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BBS/E/C/00005205, BB/K00882X/1, BB/N004825/1] Funding Source: researchfish
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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 rerunning 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 degrees 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]. (C) 2015 Elsevier B.V. All rights reserved.
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