Emulating maize yields from global gridded crop models using statistical estimates
Name
MITJPSPGC_Rpt279.pdf
Size
3.02 MB
Format
Adobe PDF
Checksum (MD5)
426e9fe317d818c8bad58b0251f6134c
Author(s)
Blanc, E.
Sultan, B.
Date Issued
March 2015
Publisher
MIT Joint Program on the Science and Policy of Global Change
Citation
Report 279
Series/Report no.
MIT Joint Program Report Series;279
Abstract
This study estimates statistical models emulating maize yield responses to changes in temperature and
precipitation simulated by global gridded crop models. We use the unique and newly-released
Inter-Sectoral Impact Model Intercomparison Project Fast Track ensemble of global gridded crop
model simulations to build a panel of annual maize yields simulations from five crop models and
corresponding monthly weather variables for over a century. This dataset is then used to estimate
statistical relationships between yields and weather variables for each crop model. The statistical
models are able to closely replicate both in- and out-of-sample maize yields projected by the crop
models. This study therefore provides simple tools to predict gridded changes in maize yields due to
climate change at the global level. By emulating crop yields for several models, the tools will be
useful for climate change impact assessments and facilitate evaluation of crop model uncertainty.
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