Crop models capture the impacts of climate variability on corn yield

Dev Niyogi, Xing Liu, Jeff Andresen, Yang Song, Atul K. Jain, Olivia Kellner, Eugene S. Takle, Otto C. Doering

Research output: Contribution to journalArticlepeer-review

Abstract

We investigate the ability of three different crop models of varying complexity for capturing El Niño-Southern Oscillation-based climate variability impacts on the U.S. Corn Belt (1981-2010). Results indicate that crop models, irrespective of their complexity, are able to capture the impacts of climate variability on yield. Multiple-model ensemble analysis provides best results. There was no significant difference between using on-site and gridded meteorological data sets to drive the models. These results highlight the ability of using simpler crop models and gridded regional data sets for crop-climate assessments. Key Points In Corn Belt, El Niño (La Niña) has positive (negative) impact on corn yield Crop models can capture the regional impacts of ENSO on yield The study highlights the advantage of simpler crop models and gridded data sets

Original languageEnglish (US)
Pages (from-to)3356-3363
Number of pages8
JournalGeophysical Research Letters
Volume42
Issue number9
DOIs
StatePublished - May 16 2015

Keywords

  • ENSO
  • U.S. Corn Belt
  • climate information
  • crop models
  • useful to useable

ASJC Scopus subject areas

  • Geophysics
  • Earth and Planetary Sciences(all)

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