Comparing and combining process-based crop models and statistical models with some implications for climate change
Abstract
We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.
Additional Information
© 2017 IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 23 November 2016. Accepted 12 July 2017. Accepted Manuscript online 12 July 2017. Published 20 September 2017.Attached Files
Published - Roberts_2017_Environ._Res._Lett._12_095010.pdf
Supplemental Material - ERL_12_9_095010_suppdata.pdf
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Additional details
- Eprint ID
- 82341
- Resolver ID
- CaltechAUTHORS:20171013-092706701
- Created
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2017-10-17Created from EPrint's datestamp field
- Updated
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2022-07-12Created from EPrint's last_modified field