Statistical Modelling 15 (6) (2015), 499–525
Emulating global climate change impacts on crop yields
Oluwole K Oyebamiji
Department of Environment,
Earth & Ecosystems,
The Open University,
Milton Keynes,
UK
e-mail: Oluwole.Oyebamiji@open.ac.uk
Neil R. Edwards
Department of Environment,
Earth & Ecosystems,
The Open University,
Milton Keynes,
UK
Philip B Holden
Department of Environment,
Earth & Ecosystems,
The Open University,
Milton Keynes,
UK
Paul H Garthwaite
Department of Mathematics & Statistics,
The Open University,
Milton Keynes,
UK
Sibyll Schaphoff
Research Domain of Earth System Analysis,
Potsdam Institute for Climate Impact Research (PIK),
Telegrafenberg A62,
14473 Potsdam,
Germany
Dieter Gerten
Research Domain of Earth System Analysis,
Potsdam Institute for Climate Impact Research (PIK),
Telegrafenberg A62,
14473 Potsdam,
Germany
Abstract:
The potential effects of climate change on the environment and society are many. In order to effectively quantify the uncertainty associated with these effects, highly complex simulation models are run with detailed representations of ecosystem processes. These models are computationally expensive and can involve a computer run of several days. Computationally cheaper models can be obtained from large ensembles of simulations using statistical emulation. The purpose of this article is to construct a cheaper computational model (emulator) from simulations of the Lund-Potsdam-Jena managed Land (LPJmL), which is a dynamic global vegetation and crop model. This article focuses on statistical emulation of potential crop yields from LPJmL and an emulator is constructed using a combination of ordinary least squares, principal component analysis and weighted least squares methods. For five climate models, under cross-validation, the percentage of variance explained ranges from 60 to 88% for the rainfed crops and 62 to 93% for the irrigated crops. The emulator can be used to predict potential crop yield change under any future climate scenarios and management options.
Keywords:
crop yield; CO2 fertilization effect; Principal components; distance weighted regression.
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